% Encoding: UTF-8
Automatically generated by Mendeley Desktop 1.19.4
Any changes to this file will be lost if it is regenerated by Mendeley.

BibTeX export options can be customized via Options -> BibTeX in Mendeley Desktop

@article{Ward2017,
abstract = {Many language learners never acquire truly native-sounding prosody, and often are weak on the dialog-related uses of prosody. Previous work has suggested this may involve deficits with specific prosodic constructions, but this has not been systematically investigated. We developed semi-automatic analysis methods able to identify and characterize such differences. Starting with two sets of dialog data, one of native speakers and one of non-natives, we applied Principal Compo-nents Analysis, and then identified differences in distributions and in the constructions themselves. Applied to recordings of six advanced-level native-Spanish learners conversing in English, these methods revealed differences in their uses of speaking rate and pitch in turn-taking, and infrequent and variant use of the English prosodic constructions for showing involvement and for explaining.},
author = {Ward, Nigel G and Org, Nigelward@acm and Gallardo, Paola and Stent, Amanda},
doi = {10.5087/dad.2017.101},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ward et al. - 2017 - Non-Native Differences in Prosodic-Construction Use.pdf:pdf},
issn = {21529620},
journal = {Dialogue {\&} Discourse},
keywords = {American English,L1 transfer,L2,Mexican-Spanish speakers,comparison,conversation,dialog,interaction,language learning,non-native prosody,pragmatics,principal components analysis,second language,semi-automatic methods,turn-taking},
number = {1},
pages = {1--30},
title = {{Non-Native Differences in Prosodic-Construction Use}},
volume = {8},
year = {2017}
}
@article{Honig2012,
abstract = {—The first part deals with general considerations on the evaluation of both human raters and automatic systems, employed for pronunciation assessment: How can we come closer to an unbiased, realistic estimate of their reliability, given the fallibility of human annotators and the nature of machine-learning algorithms (and researchers) that adapt, and inevitably overfit to a given training set. In the second part, we will present concrete models for the assessment of the overall rhythmic quality of the learner's speech. The methods are evaluated in detail on read and semi-spontaneous English data from the German research projects C-AuDiT and AUWL.},
author = {H{\"{o}}nig, Florian and Batliner, Anton and N{\"{o}}th, Elmar},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/H{\"{o}}nig, Batliner, N{\"{o}}th - 2012 - Automatic Assessment of Non-Native Prosody — Annotation, Modelling and Evaluation.pdf:pdf},
journal = {Proceedings of the International Symposium on Automatic Detection of Errors in Pronunciation Training (IS ADEPT)},
pages = {21--30},
title = {{Automatic Assessment of Non-Native Prosody — Annotation, Modelling and Evaluation}},
year = {2012}
}
@article{Arslan1996,
abstract = {It is well known that speaker variability caused by accent is one factor that degrades performance of speech recognition algorithms. If knowledge of speaker accent can be estimated accurately, then a modified set of recognition models which addresses speaker accent could be employed to increase recognition accuracy. In this study, the problem of language accent classification in American English is considered. A database of foreign language accent is established that consists of words and phrases that are known to be sensitive to accent. Next, isolated word and phoneme based accent classification algorithms are developed. The feature set under consideration includes Mel-cepstrum coefficients and energy, and their first order differences. It is shown that as test utterance length increases, higher classification accuracy is achieved. Isolated word strings of 7-8 words uttered by the speaker results in an accent classification rate of 93{\%} among four different language accents. A subjective listening test is also conducted in order to compare human performance with computer algorithm performance in accent discrimination. The results show that computer based accent classification consistently achieves superior performance over human listener responses for classification. It is shown, however, that some listeners are able to match algorithm performance for accent detection. Finally, an experimental study is performed to investigate the influence of foreign accent on speech recognition algorithms. It is shown that training separate models for each accent rather than using a single model for each word can improve recognition accuracy dramatically.},
author = {Arslan, Levent M. and Hansen, John H.L.},
doi = {10.1016/0167-6393(96)00024-6},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Arslan, Hansen - 1996 - Language accent classification in American English.pdf:pdf},
isbn = {0167-6393},
issn = {01676393},
journal = {Speech Communication},
keywords = {Accent analysis,Accent classification,Hidden markov models,Listener accent assessment,Robust speech recognition},
number = {4},
pages = {353--367},
title = {{Language accent classification in American English}},
volume = {18},
year = {1996}
}
@article{Wu2010,
abstract = {In this paper, we develop methods to identify accents of native speakers. Accent identification differs from other speaker classification tasks because accents may differ in a limited number of phonemes only and moreover the differences can be quite subtle. In this paper, it is shown that in such cases it is essential to select a small subset of discriminative features that can be reliably estimated and at the same time discard non-discriminative and noisy features. For identification purposes a speaker is modeled by a supervector containing the mean values for the features for all phonemes. Initial accent models are obtained as class means from the speaker supervectors. Then feature subset selection is performed by applying either ANOVA (analysis of variance), LDA (linear discriminant analysis), SVM-RFE (support vector machine-recursive feature elimination), or their hybrids, resulting in a reduced dimensionality of the speaker vector and more importantly a significantly enhanced recognition performance. We also compare the performance of GMM, LDA and SVM as classifiers on a full or a reduced feature subset. The methods are tested on a Flemish read speech database with speakers classified in five regions. The difficulty of the task is confirmed by a human listening experiment. We show that a relative improvement of more than 20{\%} in accent recognition rate can be achieved with feature subset selection irrespective of the choice of classifier. We finally show that the construction of speaker-based supervectors significantly enhances results over a reference GMM system that uses the raw feature vectors directly as input, both in text dependent and independent conditions. {\textcopyright} 2009 Elsevier B.V. All rights reserved.},
author = {Wu, Tingyao and Duchateau, Jacques and Martens, Jean Pierre and {Van Compernolle}, Dirk},
doi = {10.1016/j.specom.2009.08.010},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wu et al. - 2010 - Feature subset selection for improved native accent identification.pdf:pdf},
isbn = {0167-6393},
issn = {01676393},
journal = {Speech Communication},
keywords = {Accent identification,Feature selection,Gaussian mixture model,Language identification,Linear discriminant analysis,Support vector machine},
number = {2},
pages = {83--98},
title = {{Feature subset selection for improved native accent identification}},
volume = {52},
year = {2010}
}
@article{Schuller2013,
abstract = {Paralinguistic analysis is increasingly turning into a mainstream topic in speech and language processing. This article aims to provide a broad overview of the constantly growing field by defining the field, introducing typical applications, presenting exemplary resources, and sharing a unified view of the chain of processing. It then presents the first broader Paralinguistic Challenge organised at INTERSPEECH 2010 by the authors including a historical overview of the Challenge tasks of recognising age, gender, and affect, a summary of methods used by the participants, and their results. In addition, we present the new benchmark obtained by fusion of participants' predictions and conclude by discussing ten recent and emerging trends in the analysis of paralinguistics in speech and language. {\textcopyright} 2012 Elsevier Ltd. All rights reserved.},
author = {Schuller, Bj{\"{o}}rn and Steidl, Stefan and Batliner, Anton and Burkhardt, Felix and Devillers, Laurence and M{\"{u}}ller, Christian and Narayanan, Shrikanth},
doi = {10.1016/j.csl.2012.02.005},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Schuller et al. - 2013 - Paralinguistics in speech and language - State-of-the-art and the challenge.pdf:pdf},
isbn = {0885-2308},
issn = {08852308},
journal = {Computer Speech and Language},
keywords = {Affect,Age,Challenge,Gender,Paralinguistics,Survey,Trends},
number = {1},
pages = {4--39},
pmid = {10952691},
publisher = {Elsevier Ltd},
title = {{Paralinguistics in speech and language - State-of-the-art and the challenge}},
url = {http://dx.doi.org/10.1016/j.csl.2012.02.005},
volume = {27},
year = {2013}
}

@Article{ll,
  author   = {Barnard, Etienne and Cole, Ronald A.},
  title    = {{Reviewing Automatic Language Identification}},
  journal  = {IEEE Signal Processing Magazine},
  year     = {1994},
  volume   = {11},
  number   = {4},
  pages    = {33--41},
  issn     = {10535888},
  abstract = {The Oregon Graduate Institute Multi-language Telephone Speech$\backslash$nCorpus (OGI-TS) was designed specifically for language identification$\backslash$nresearch. It currently consists of spontaneous and fixed-vocabulary$\backslash$nutterances in 11 languages: English, Farsi, French, German, Hindi,$\backslash$nJapanese, Korean, Mandarin, Spanish, Tamil, and Vietnamese. These$\backslash$nutterances were produced by 90 native speakers in each language over$\backslash$nreal telephone lines. Language identification is related to$\backslash$nspeaker-independent speech recognition and speaker identification in$\backslash$nseveral interesting ways. It is therefore not surprising that many of$\backslash$nthe recent developments in language identification can be related to$\backslash$ndevelopments in those two fields. We review some of the more important$\backslash$nrecent approaches to language identification against the background of$\backslash$nsuccesses in speaker and speech recognition. In particular, we$\backslash$ndemonstrate how approaches to language identification based on acoustic$\backslash$nmodeling and language modeling, respectively, are similar to algorithms$\backslash$nused in speaker-independent continuous speech recognition. Thereafter,$\backslash$nprosodic and duration-based information sources are studied. We then$\backslash$nreview an approach to language identification that draws heavily on$\backslash$nspeaker identification. Finally, the performance of some representative$\backslash$nalgorithms is reported},
  doi      = {10.1109/79.317925},
  file     = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Barnard, Cole - 1994 - Reviewing Automatic Language Identification.pdf:pdf},
  isbn     = {1053-5888},
}

@Article{Franco2000,
  author   = {Franco, Horacio and Neumeyer, Leonardo and Digalakis, Vassilios and Ronen, Orith},
  title    = {{Combination of machine scores for automatic grading of pronunciation quality}},
  journal  = {Speech Communication},
  year     = {2000},
  volume   = {30},
  number   = {2},
  pages    = {121--130},
  issn     = {01676393},
  abstract = {This work is part of an effort aimed at developing computer-based systems for language instruction; we address the task of grading the pronunciation quality of the speech of a student of a foreign language. The automatic grading system uses SRI's DecipherTMcontinuous speech recognition system to generate phonetic segmentations. Based on these segmentations and probabilistic models we produce different pronunciation scores for individual or groups of sentences that can be used as predictors of the pronunciation quality. Different types of these machine scores can be combined to obtain a better prediction of the overall pronunciation quality. In this paper we review some of the best-performing machine scores and discuss the application of several methods based on linear and nonlinear mapping and combination of individual machine scores to predict the pronunciation quality grade that a human expert would have given. We evaluate these methods in a database that consists of pronunciation-quality-graded speech from American students speaking French. With predictors based on spectral match and on durational characteristics, we find that the combination of scores improved the prediction of the human grades and that nonlinear mapping and combination methods performed better than linear ones. Characteristics of the different nonlinear methods studied are discussed.},
  doi      = {10.1016/S0167-6393(99)00045-X},
  file     = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Franco et al. - 2000 - Combination of machine scores for automatic grading of pronunciation quality.pdf:pdf},
  isbn     = {0167-6393},
  keywords = {automatic pronunciation scoring,combination of scores,computer aided language learning,hidden markov models,language instruction systems,pronunciation,quality assessment,speech recognition},
}
@article{Shriberg2005,
abstract = {We describe a novel approach to modeling idiosyncratic prosodic behavior for automatic speaker recognition. The approach computes various duration, pitch, and energy features for each estimated syllable in speech recognition output, quantizes the features, forms N-grams of the quantized values, and models normalized counts for each feature N-gram using support vector machines (SVMs). We refer to these features as "SNERF-grams" (N-grams of Syllable-based Nonuniform Extraction Region Features). Evaluation of SNERF-gram performance is conducted on two-party spontaneous English conversational telephone data from the Fisher corpus, using one conversation side in both training and testing. Results show that SNERF-grams provide significant performance gains when combined with a state-of-the-art baseline system, as well as with two highly successful long-range feature systems that capture word usage and lexically constrained duration patterns. Further experiments examine the relative contributions of features by quantization resolution, N-gram length, and feature type. Results show that the optimal number of bins depends on both feature type and N-gram length, but is roughly in the range of 5-10 bins. We find that longer N-grams are better than shorter ones, and that pitch features are most useful, followed by duration and energy features. The most important pitch features are those capturing pitch level, whereas the most important energy features reflect patterns of rising and falling. For duration features, nucleus duration is more important for speaker recognition than are durations from the onset or coda of a syllable. Overall, we find that SVM modeling of prosodic feature sequences yields valuable information for automatic speaker recognition. It also offers rich new opportunities for exploring how speakers differ from each other in voluntary but habitual ways. {\textcopyright} 2005 Elsevier B.V. All rights reserved.},
author = {Shriberg, E. and Ferrer, L. and Kajarekar, S. and Venkataraman, A. and Stolcke, A.},
doi = {10.1016/j.specom.2005.02.018},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Shriberg et al. - 2005 - Modeling prosodic feature sequences for speaker recognition.pdf:pdf},
isbn = {0167-6393},
issn = {01676393},
journal = {Speech Communication},
keywords = {Automatic speaker recognition,Prosody,Speaker verification,Support vector machines},
number = {3-4},
pages = {455--472},
pmid = {26405004},
title = {{Modeling prosodic feature sequences for speaker recognition}},
volume = {46},
year = {2005}
}
@article{Neumeyer2000,
abstract = {We present a paradigm for the automatic assessment of pronunciation quality by machine. In this scoring paradigm, both native and nonnative speech data is collected and a database of human-expert ratings is created to enable the development of a variety of machine scores. We first discuss issues related to the design of speech databases and the reliability of human ratings. We then address pronunciation evaluation as a prediction problem, trying to predict the grade a human expert would assign to a particular skill. Using the speech and the expert-ratings databases, we build statistical models and introduce different machine scores that can be used as predictor variables. We validate these machine scores on the Voice Interactive Language Training System (VILTS) corpus, evaluating the pronunciation of American speakers speaking French and we show that certain machine scores, like the log-posterior and the normalized duration, achieve a correlation with the targeted human grades that is comparable to the human-to-human correlation when a sufficient amount of speech data is available.},
author = {Neumeyer, Leonardo and Franco, Horacio and Digalakis, Vassilios and Weintraub, Mitchel},
doi = {10.1016/S0167-6393(99)00046-1},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Neumeyer et al. - 2000 - Automatic scoring of pronunciation quality.pdf:pdf},
isbn = {0167-6393},
issn = {01676393},
journal = {Speech Communication},
keywords = {assessment,automatic pronunciation scoring,computer aided language learning,hidden markov models,language instruction systems,pronunciation quality,speech recognition,speech technology},
number = {2},
pages = {83--93},
title = {{Automatic scoring of pronunciation quality}},
volume = {30},
year = {2000}
}
@article{Arias2010,
abstract = {In this paper the nature and relevance of the information provided by intonation is discussed in the framework of second language learning. As a consequence, an automatic intonation assessment system for second language learning is proposed based on a top-down scheme. A stress assessment system is also presented by combining intonation and energy contour estimation. The utterance pronounced by the student is directly compared with a reference one. The trend similarity of intonation and energy contours are compared frame-by-frame by using DTW alignment. Moreover the robustness of the alignment provided by the DTW algorithm to microphone, speaker and quality pronunciation mismatch is addressed. The intonation assessment system gives an averaged subjective-objective score correlation as high as 0.88. The stress assessment evaluation system gives an EER equal to 21.5{\%}, which in turn is similar to the error observed in phonetic quality evaluation schemes. These results suggest that the proposed systems could be employed in real applications. Finally, the schemes presented here are text- and language-independent due to the fact that the reference utterance text-transcription and language are not required. {\textcopyright} 2009 Elsevier B.V. All rights reserved.},
author = {Arias, Juan Pablo and Yoma, Nestor Becerra and Vivanco, Hiram},
doi = {10.1016/j.specom.2009.11.001},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Arias, Yoma, Vivanco - 2010 - Automatic intonation assessment for computer aided language learning.pdf:pdf},
isbn = {0167-6393},
issn = {01676393},
journal = {Speech Communication},
keywords = {Computer aided language learning,Intonation assessment,Word stress assessment},
number = {3},
pages = {254--267},
publisher = {Elsevier B.V.},
title = {{Automatic intonation assessment for computer aided language learning}},
url = {http://dx.doi.org/10.1016/j.specom.2009.11.001},
volume = {52},
year = {2010}
}
@article{Mary2008,
abstract = {In this paper, we propose a new approach for extracting and representing prosodic features directly from the speech signal. We hypothesize that prosody is linked to linguistic units such as syllables, and it is manifested in terms of changes in measurable parameters such as fundamental frequency (F0), duration and energy. In this work, syllable-like unit is chosen as the basic unit for representing the prosodic characteristics. Approximate segmentation of continuous speech into syllable-like units is obtained by locating the vowel onset points (VOP) automatically. The knowledge of the VOPs serve as reference for extracting prosodic features from the speech signal. Quantitative parameters are used to represent F0and energy contour in each region between two consecutive VOPs. Prosodic features extracted using this approach may be useful in applications such as recognition of language or speaker, where explicit phoneme/syllable boundaries are not easily available. The effectiveness of the derived prosodic features for language and speaker recognition is evaluated in the case of NIST language recognition evaluation 2003 and the extended data task of NIST speaker recognition evaluation 2003, respectively. {\textcopyright} 2008 Elsevier B.V. All rights reserved.},
author = {Mary, Leena and Yegnanarayana, B.},
doi = {10.1016/j.specom.2008.04.010},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mary, Yegnanarayana - 2008 - Extraction and representation of prosodic features for language and speaker recognition.pdf:pdf},
isbn = {978-1-4614-1158-1},
issn = {01676393},
journal = {Speech Communication},
keywords = {Autoassociative neural network,Intonation,Language recognition,Multilayer feedforward neural network,Prosody,Rhythm,Speaker recognition,Stress,Vowel onset point},
number = {10},
pages = {782--796},
publisher = {Elsevier B.V.},
title = {{Extraction and representation of prosodic features for language and speaker recognition}},
url = {http://dx.doi.org/10.1016/j.specom.2008.04.010},
volume = {50},
year = {2008}
}
@article{ValenciaArroyave2012,
author = {{Valencia Arroyave}, Felipe},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Valencia Arroyave - 2012 - Game Theory Based Distributed Model Predictive Control An Approach to Large-Scale Systems Control.pdf:pdf},
pages = {126},
title = {{Game Theory Based Distributed Model Predictive Control : An Approach to Large-Scale Systems Control}},
year = {2012}
}
@article{Archive2015,
author = {Archive, Online},
isbn = {9788055406060},
title = {{ARSA , Advanced Research in Scientific Areas :: Conference Online Archive ARSA , Advanced Research in Scientific Areas :: Conference Online Archive}},
year = {2015}
}
@article{Mallat1999,
abstract = {Fourier presented a memoir to the Institut de France in 1807 where he claimed that any periodic function can be represented as a series of harmonically related sinusoids. This idea had a profound impact in mathematical analysis, physics and engineering, but it took one and a half centuries to understand the convergence of Fourier series and complete the theory of Fourier integrals. Fourier was motivated by the study of heat diffusion, which is governed by a linear differential equation. However, the Fourier transform diagonalizes all linear time-invariant operators, which are the building blocks of signal processing. It is, therefore, not only the starting point of our exploration but the basis of all further developments. Two -dimensional Fourier Transform is a straightforward extension of the one-dimensional Fourier transform. The two-dimensional case is briefly reviewed for image processing applications.},
author = {Mallat, St{\'{e}}phane},
doi = {10.1016/B978-012466606-1/50004-0},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mallat - 1999 - A Wavelet Tour of Signal Processing.pdf:pdf},
isbn = {9780124666061},
issn = {10538801},
journal = {A Wavelet Tour of Signal Processing},
pages = {20--41},
title = {{A Wavelet Tour of Signal Processing}},
url = {http://www.sciencedirect.com/science/article/pii/B9780124666061500040},
year = {1999}
}
@article{Omar2010,
abstract = {Abstract Recent longitudinal studies in dietary daily intake in human centenarians have shown that a satisfactory content of some micronutrients within the cells maintain several immune functions, a low grade of inflammation and preserve antioxidant activity. Micronutrients (zinc, copper, selenium) play a pivotal role in maintaining and reinforcing the performances of the immune and antioxidant systems as well as in affecting the complex network of the genes (nutrigenomic) with anti- and pro-inflammatory tasks. Genes of pro- and anti-inflammatory cytokines and some key regulators of trace elements homeostasis, such as Metallothioneins (MT), are involved in the susceptibility to major geriatric disease/disorders. Moreover, the genetic inter-individual variability may affect the nutrients{\^{a}}€™ absorption (nutrigenetic) with altered effects on inflammatory/immune response and antioxidant activity. The interaction between genetic factors and micronutrients (nutrigenomic and nutrigenetic approaches) may influence ageing and longevity because the micronutrients may become also toxic. This review reports the micronutrient{\^{a}}€“gene interactions in ageing and their impact on the healthy state with a focus on the method of protein{\^{a}}€“metal speciation analysis. The association between micronutrient{\^{a}}€“gene interactions and the protein{\^{a}}€“metal speciation analysis can give a complete picture for a personalized nutrient supplementation or chelation in order to reach healthy ageing and longevity.},
author = {Omar, Mohamed Kamal and Pelecanos, Jason},
doi = {10.1109/ICASSP.2010.5495628},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Omar, Pelecanos - 2010 - A novel approach to detecting non-native speakers and their native language.pdf:pdf},
isbn = {9781424442966},
issn = {15206149},
journal = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings},
keywords = {Accent detection,K-means clustering,Nonnative speaker detection,Speaker verification},
pages = {4398--4401},
title = {{A novel approach to detecting non-native speakers and their native language}},
year = {2010}
}
@article{Tseng2006,
author = {Tseng, Ivy H and Verscheure, Olivier and Turaga, Deepak S and Chaudhari, Upendra V},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Tseng et al. - 2006 - QUANTIZATION FOR ADAPTED GMM-BASED SPEAKER VERIFICATION Signal and Image Processing Institute , University of Sout.pdf:pdf},
pages = {653--656},
title = {{QUANTIZATION FOR ADAPTED GMM-BASED SPEAKER VERIFICATION Signal and Image Processing Institute , University of Southern California , Los Angeles , CA 90089}},
year = {2006}
}
@article{Zampieri2017,
abstract = {This paper presents an ensemble system combining the output of multiple SVM classifiers to native language identification (NLI). The system was submitted to the NLI Shared Task 2017 fusion track which featured students essays and spoken responses in form of audio transcriptions and iVectors by non-native English speakers of eleven native languages. Our system competed in the challenge under the team name ZCD and was based on an ensemble of SVM classifiers trained on character n-grams achieving 83.58{\%} accuracy and ranking 3rd in the shared task.},
archivePrefix = {arXiv},
arxivId = {1707.07182},
author = {Zampieri, Marcos and Ciobanu, Alina Maria and Dinu, Liviu P.},
eprint = {1707.07182},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Zampieri, Ciobanu, Dinu - 2017 - Native Language Identification on Text and Speech.pdf:pdf},
pages = {398--404},
title = {{Native Language Identification on Text and Speech}},
url = {http://arxiv.org/abs/1707.07182},
volume = {2013},
year = {2017}
}
@article{Moustroufas2007,
abstract = {In this study we present various techniques to evaluate the pronunciation of students of a foreign language without any knowledge of the uttered text. Previous attempts have shown that it is feasible to evaluate the pronunciation of a non-native speaker by having implicit or explicit knowledge of the uttered text, provided that enough utterances are available. Our approach is to use characteristics of the mother tongue (SOURCE language) of the speaker in the evaluation of his/her pronunciation. We recorded 20 Greek students speaking English (TARGET language) and evaluated their pronunciation using algorithms that include characteristics of the SOURCE language (Greek). We show that the pronunciation scores that are based on both TARGET- and SOURCE-language characteristics have better correlation with the human scores than those based only on characteristics of the TARGET language. As in previous studies, we found that the best-performing algorithms for automatic evaluation of pronunciation are based on speech recognition technology. {\textcopyright} 2006 Elsevier Ltd. All rights reserved.},
author = {Moustroufas, N. and Digalakis, V.},
doi = {10.1016/j.csl.2006.04.001},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Moustroufas, Digalakis - 2007 - Automatic pronunciation evaluation of foreign speakers using unknown text.pdf:pdf},
issn = {08852308},
journal = {Computer Speech and Language},
number = {1},
pages = {219--230},
title = {{Automatic pronunciation evaluation of foreign speakers using unknown text}},
volume = {21},
year = {2007}
}
@article{Wang2011,
abstract = {This paper deals with Wiener model based predictive control of a pH neutralization process. The dynamic linear block of the Wiener model is parameterized using Laguerre filters while the nonlinear block is constructed using least squares support vector machines (LSSVM). Input-output data from the first principle model of the pH neutralization process are used for the Wiener model identification. Simulation results show that the proposed Wiener model has higher prediction accuracy than Laguerre-support vector regression (SVR) Wiener models, Laguerre-polynomial Wiener models, and linear Laguerre models. The identified Wiener model is used here for nonlinear model predictive control (NMPC) of the pH neutralization process. The set-point tracking performance of the proposed NMPC is compared with those of the Laguerre-SVR Wiener model based NMPC, Laguerre-polynomial Wiener model based NMPC, and linear model predictive control (LMPC). Validation results show that the proposed NMPC outperforms the other three controllers.},
author = {Wang, Qing-chao and Zhang, Jian-zhong},
doi = {10.1631/jzus.C0910779},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wang, Zhang - 2011 - Wiener model identification and nonlinear model predictive control of a pH neutralization process based on Laguerre.pdf:pdf},
issn = {1869-1951},
journal = {Journal of Zhejiang University SCIENCE C},
keywords = {10,1631,c0910779,doi,jzus,laguerre filters,least,lssvm,nmpc,nonlinear model predictive control,ph neutralization process,squares support vector machines,wiener model},
number = {1},
pages = {25--35},
title = {{Wiener model identification and nonlinear model predictive control of a pH neutralization process based on Laguerre filters and least squares support vector machines}},
volume = {12},
year = {2011}
}
@article{Paalanen2006,
abstract = {This study promotes the use of statistical methods in specific classification tasks since statistical methods have certain advantages which advocate their use in pattern recognition. One central problem in statistical methods is estimation of class conditional probability density functions based on examples in a training set. In this study maximum likelihood estimation methods for Gaussian mixture models are reviewed and discussed from a practical point of view. In addition, good practices for utilizing probability densities in feature classification and selection are discussed for Bayesian and, more importantly, for non-Bayesian tasks. As a result, the use of confidence information in the classification is proposed and a method for confidence estimation is presented. The propositions are tested experimentally. {\textcopyright} 2006 Pattern Recognition Society.},
author = {Paalanen, Pekka and Kamarainen, Joni Kristian and Ilonen, Jarmo and K{\"{a}}lvi{\"{a}}inen, Heikki},
doi = {10.1016/j.patcog.2006.01.005},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Paalanen et al. - 2006 - Feature representation and discrimination based on Gaussian mixture model probability densities-Practices and a.pdf:pdf},
isbn = {952214021X},
issn = {00313203},
journal = {Pattern Recognition},
keywords = {Classifier,Confidence,EM,Gaussian mixture model,Highest density region},
number = {7},
pages = {1346--1358},
title = {{Feature representation and discrimination based on Gaussian mixture model probability densities-Practices and algorithms}},
volume = {39},
year = {2006}
}
@article{Li2013,
abstract = {Spoken language recognition refers to the auto- matic process through which we determine or verify the identity of the language spoken in a speech sample. We study a computational framework that allows such a decision to be made in a quantitative manner. In recent decades, we have made tremendous progress in spoken language recognition, which benefited from technological breakthroughs in related areas, such as signal processing, pattern recognition, cognitive science, and machine learning. In this paper, we attempt to provide an introductory tutorial on the fundamentals of the theory and the state-of-the-art solutions, from both phonolog- ical and computational aspects. We also give a comprehensive review of current trends and future research directions using the language recognition evaluation (LRE) formulated by the National Institute of Standards and Technology (NIST) as the case studies.},
author = {Li, Haizhou and Ma, Bin and Lee, Kong Aik},
doi = {10.1109/JPROC.2012.2237151},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Li, Ma, Lee - 2013 - Spoken language recognition From fundamentals to practice.pdf:pdf},
isbn = {0018-9219},
issn = {00189219},
journal = {Proceedings of the IEEE},
keywords = {Acoustic features,calibration,classifier,fusion,language recognition evaluation (LRE),phonotactic features,spoken language recognition,tokenization,vector space modeling},
number = {5},
pages = {1136--1159},
title = {{Spoken language recognition: From fundamentals to practice}},
volume = {101},
year = {2013}
}
@article{Farrus2010,
abstract = {Voices can be deliberately disguised by means of human imitation or voice conversion. The question arises to what extent they can be modified by using either method. In the current paper, a set of speaker identification experiments are conducted; first, analysing some prosodic features extracted from voices of professional impersonators attempting to mimic a target voice and, second, using both intragender and crossgender converted voices in a spectral-based speaker recognition system. The results obtained in the current experiments show that the identification error rate increases when testing with imitated voices, as well as when using converted voices, especially the crossgender conversions.},
author = {Farr{\'{u}}s, Mireia and Wagner, Michael and Erro, Daniel and Hernando, Javier},
doi = {10.1558/ijsll.v17i1.119},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Farr{\'{u}}s et al. - 2010 - Automatic speaker recognition as a measurement of voice imitation and conversion.pdf:pdf},
isbn = {Farr{\'{u}}s, M. [et al.]. Automatic speaker recognition as a measurement of voice imitation and conversion. "The Intenational Journal of Speech. Language and the Law", 01 Gener 2010, vol. 1, n{\'{u}}m. 17, p. 119-142.},
issn = {17488885},
journal = {International Journal of Speech, Language and the Law},
keywords = {Imitation,Jitter,Prosody,Shimmer,Speaker recognition,Voice conversion},
number = {1},
pages = {119--142},
title = {{Automatic speaker recognition as a measurement of voice imitation and conversion}},
volume = {17},
year = {2010}
}

@Article{Anumanchipalli2013,
  author  = {Gopala Krishna Anumanchipalli, LuisC Oliviera, and Alan W Black},
  title   = {{Accent Group Modeling For Improved Prosody in Statistical Parametric Speech Synthesis}},
  journal = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, Pages 6890-6894},
  year    = {2013},
  pages   = {6890--6894},
  file    = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Anumanchipalli - 2013 - ACCENT GROUP MODELING FOR IMPROVED PROSODY IN STATISTICAL PARAMETERIC SPEECH SYNTHESIS Language Technologies Ins.pdf:pdf},
  isbn    = {9781479903566},
}
@article{Chen2014,
abstract = {In this work, we propose a framework that automatically discovers dialect-specific phonetic rules. These rules characterize when certain phonetic or acoustic transformations occur across dialects. To explicitly characterize these dialect-specific rules, we adapt the conventional hidden Markov model to handle insertion and deletion transformations. The proposed framework is able to convert pronunciation of one dialect to another using learned rules, recognize dialects using learned rules, retrieve dialect-specific regions, and refine linguistic rules. Potential applications of our proposed framework include computer-assisted language learning, sociolinguistics, and diagnosis tools for phonological disorders. {\textcopyright} 2013 IEEE.},
author = {Chen, Nancy F. and Tam, Sharon W. and Shen, Wade and Campbell, Joseph P.},
doi = {10.1109/TASLP.2013.2285482},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Chen et al. - 2014 - Characterizing phonetic transformations and acoustic differences across English dialects.pdf:pdf},
issn = {15587916},
journal = {IEEE Transactions on Audio, Speech and Language Processing},
keywords = {Accent,Informative dialect recognition,Phonological rules,Phonotactic modeling,Pronunciation model},
number = {1},
pages = {110--124},
title = {{Characterizing phonetic transformations and acoustic differences across English dialects}},
volume = {22},
year = {2014}
}
@article{Ghorbani2018,
abstract = {Recognition of accented speech is a long-standing challenge for automatic speech recognition (ASR) systems, given the increasing worldwide population of bilingual speakers with English as their second language. If we consider foreign-accented speech as an interpolation of the native language (L1) and English (L2), using a model that can simultaneously address both languages would perform better at the acoustic level for accented speech. In this study, we explore how an end-to-end recurrent neural network (RNN) trained system with English and native languages (Spanish and Indian languages) could leverage data of native languages to improve performance for accented English speech. To this end, we examine pre-training with native languages , as well as multi-task learning (MTL) in which the main task is trained with native English and the secondary task is trained with Spanish or Indian Languages. We show that the proposed MTL model performs better than the pre-training approach and outperforms a baseline model trained simply with English data. We suggest a new setting for MTL in which the secondary task is trained with both English and the native language , using the same output set. This proposed scenario yields better performance with +11.95{\%} and +17.55{\%} character error rate gains over baseline for Hispanic and Indian accents, respectively .},
author = {Ghorbani, Shahram and Hansen, John H L and Speech, Robust and Crss, Systems},
doi = {10.21437/Interspeech.2018-1378},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ghorbani et al. - 2018 - Leveraging native language information for improved accented speech recognition.pdf:pdf},
issn = {19909772},
number = {September},
pages = {2449--2453},
title = {{Leveraging native language information for improved accented speech recognition}},
year = {2018}
}
@article{Lopez2014,
author = {L{\'{o}}pez, Jes{\'{u}}s Antonio Villalba},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/L{\'{o}}pez - 2014 - Advances on Speaker Recognition in non Collaborative Environments.pdf:pdf},
pages = {311},
title = {{Advances on Speaker Recognition in non Collaborative Environments}},
year = {2014}
}
@article{Goethals2005,
abstract = {This paper studies a method for the identification of Hammerstein models based on least squares support vector machines (LS-SVMs). The technique allows for the determination of the memoryless static nonlinearity as well as the estimation of the model parameters of the dynamic ARX part. This is done by applying the equivalent of Bai's overparameterization method for identification of Hammerstein systems in an LS-SVM context. The SISO as well as the MIMO identification cases are elaborated. The technique can lead to significant improvements with respect to classical overparameterization methods as illustrated in a number of examples. Another important advantage is that no stringent assumptions on the nature of the nonlinearity need to be imposed except for a certain degree of smoothness. ?? 2005 Elsevier Ltd. All rights reserved.},
author = {Goethals, Ivan and Pelckmans, Kristiaan and Suykens, Johan a K and {De Moor}, Bart},
doi = {10.1016/j.automatica.2005.02.002},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Goethals et al. - 2005 - Identification of MIMO Hammerstein models using least squares support vector machines.pdf:pdf},
issn = {00051098},
journal = {Automatica},
keywords = {ARX,Hammerstein models,Kernel methods,LS-SVM,MIMO systems},
number = {7},
pages = {1263--1272},
title = {{Identification of MIMO Hammerstein models using least squares support vector machines}},
volume = {41},
year = {2005}
}
@article{Neumeyer2000a,
abstract = {We present a paradigm for the automatic assessment of pronunciation quality by machine. In this scoring paradigm, both native and nonnative speech data is collected and a database of human-expert ratings is created to enable the development of a variety of machine scores. We first discuss issues related to the design of speech databases and the reliability of human ratings. We then address pronunciation evaluation as a prediction problem, trying to predict the grade a human expert would assign to a particular skill. Using the speech and the expert-ratings databases, we build statistical models and introduce different machine scores that can be used as predictor variables. We validate these machine scores on the Voice Interactive Language Training System (VILTS) corpus, evaluating the pronunciation of American speakers speaking French and we show that certain machine scores, like the log-posterior and the normalized duration, achieve a correlation with the targeted human grades that is comparable to the human-to-human correlation when a sufficient amount of speech data is available.},
author = {Neumeyer, Leonardo and Franco, Horacio and Digalakis, Vassilios and Weintraub, Mitchel},
doi = {10.1016/S0167-6393(99)00046-1},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Neumeyer et al. - 2000 - Automatic scoring of pronunciation quality(2).pdf:pdf},
isbn = {0167-6393},
issn = {01676393},
journal = {Speech Communication},
keywords = {assessment,automatic pronunciation scoring,computer aided language learning,hidden markov models,language instruction systems,pronunciation quality,speech recognition,speech technology},
number = {2},
pages = {83--93},
title = {{Automatic scoring of pronunciation quality}},
volume = {30},
year = {2000}
}
@article{Harper,
author = {Harper, Mary P and Maxwell, Michael},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Harper, Maxwell - Unknown - SPOKEN LANGUAGE CHARACTERIZATION Center for the Advanced Study of Language University of Maryland College Pa.pdf:pdf},
journal = {Speech Communication},
pages = {1--16},
title = {{SPOKEN LANGUAGE CHARACTERIZATION Center for the Advanced Study of Language University of Maryland College Park , MD 20742 {\{} mharper , mmaxwell {\}}@ casl . umd . edu}}
}

@Article{Bahari2013,
  author  = {Bahari, Mohamad Hasan and Saeidi, Rahim and Hamme, Hugo Van and Leeuwen, David Van},
  title   = {{Accent Recognition Using I-vector , Gaussian Mean Supervector and Gaussian Posterior probability Supervector for Spontaneous Telephone Speech}},
  journal = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing},
  year    = {2013},
  pages   = {7344--7348},
  file    = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bahari et al. - 2013 - ACCENT RECOGNITION USING I-VECTOR , GAUSSIAN MEAN SUPERVECTOR AND GAUSSIAN POSTERIOR PROBABILITY SUPERVECTOR FOR.pdf:pdf},
  isbn    = {9781479903566},
}
@article{Graham2018,
abstract = {The objective of the present study was to investigate (1) whether, and to what degree, late bilinguals of different L1 backgrounds are comparable to native speakers in the phonetic implementation of tonal targets in their L2, (2) whether they exhibit general patterns of acquisition irrespective of the typological closeness of their L1 to their L2, and (3) whether learners' choice of accent contours and the alignment of the high tone (H∗) proceeds in parallel with proficiency in the L2. More specifically, we examined the acquisition of the nuclear contour composition and the H∗alignment of the American English (L)H∗L- (i.e. pitch accent and boundary tone combination) in initial-stressed and final-stressed words by Japanese and Spanish late bilingual speakers at varying proficiency levels in American English. Our results show that the L1 Spanish speakers were more comparable than the L1 Japanese speakers to the native English speakers in the phonological aspect of intonation (choice of pitch accent contour). In terms of peak alignment, we found that the late bilinguals generally tended to realise significantly later alignment than the native speakers, although the precise manifestation of this varied according to the L1 background of speakers and the stress pattern of words.},
author = {Graham, Calbert and Post, Brechtje},
doi = {10.1016/j.wocn.2017.08.002},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Graham, Post - 2018 - Second language acquisition of intonation Peak alignment in American English.pdf:pdf},
isbn = {0095-4470},
issn = {00954470},
journal = {Journal of Phonetics},
keywords = {Acquisition,Alignment,English,Japanese,Pitch accent,Spanish},
pages = {1--14},
publisher = {Elsevier Ltd},
title = {{Second language acquisition of intonation: Peak alignment in American English}},
url = {https://doi.org/10.1016/j.wocn.2017.08.002},
volume = {66},
year = {2018}
}
@book{Lofberg2001,
author = {L{\"{o}}fberg, Johan},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/L{\"{o}}fberg - 2001 - Linear model predictive control Stability and robustness.pdf:pdf},
isbn = {9172199202},
number = {866},
title = {{Linear model predictive control: Stability and robustness}},
url = {http://www.diva-portal.org/smash/record.jsf?pid=diva2:652274},
year = {2001}
}
@article{Li2017,
abstract = {This paper investigates the use of multi-distribution deep neural networks (MD-DNNs) for automatic intonation classification in second-language (L2) English speech. If a classified intonation is different from the target one, we consider that mispronunciation is detected and appropriate diagnostic feedback can be provided thereafter. To transcribe speech data for intonation classification, we propose the RULF labels which are used to transcribe an intonation as rising, upper, lower or falling. These four types of labels can be further merged into two groups – rising and falling. Based on the annotated data from 100 Mandarin and 100 Cantonese learners, we develop an intonation classifier, which considers only 8 frames (i.e., 80 ms) of pitch value prior to the end of the pitch contour over an intonational phrase (IP). This classifier determines the intonation of L2 English speech as either rising or falling with an accuracy of 93.0{\%}.},
author = {Li, Kun and Wu, Xixin and Meng, Helen},
doi = {10.1016/j.csl.2016.11.006},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Li, Wu, Meng - 2017 - Intonation classification for L2 English speech using multi-distribution deep neural networks.pdf:pdf},
issn = {10958363},
journal = {Computer Speech and Language},
keywords = {Deep neural networks,Intonation,L2 English speech,Language learning,Pitch accent,Suprasegmental},
pages = {18--33},
publisher = {Elsevier Ltd},
title = {{Intonation classification for L2 English speech using multi-distribution deep neural networks}},
url = {http://dx.doi.org/10.1016/j.csl.2016.11.006},
volume = {43},
year = {2017}
}
@article{Ikeno2006,
abstract = {There are many aspects of speech that can provide information about a particular speaker's characteristics. Accent is a linguistic trait of speaker identity. It indicates the speaker's language and social background. The goal of this study is to provide perceptual human recognition of English native accent variation for accent and dialect identification applications. To examine relationships of the listener's accent background with perceived accent and comprehension of the speech, perceptual experiments are conducted with three types of listeners - US and British native English listeners, and nonnative English listeners. The tasks are accent detection and classification, and transcription of the speech. The results from the study show that listeners' accent background significantly impacts accent perception. The results also indicate that listeners use perceptual cues differently based on the task. Our analysis also suggests that comprehensibility of the speech affects accuracy of accent detection and classification. These observations point to the complex nature of the cognitive process involved in accent perception, which is bidirectional (bottom-up and top-down processing) and multi-dimensional (speech perception, language comprehension, etc.). This suggests the importance of understanding accent variation from a cognitive perspective for further development of accent and dialect identification systems as well as speech processing algorithms in general},
author = {Ikeno, A. and Hansen, J.H.L.},
doi = {10.1109/ICASSP.2006.1660042},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ikeno, Hansen - 2006 - Perceptual Recognition Cues in Native English Accent Variation Listener Accent, Perceived Accent, and Comprehensi.pdf:pdf},
isbn = {1-4244-0469-X},
issn = {1520-6149},
journal = {2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings},
pages = {I--401--I--404},
title = {{Perceptual Recognition Cues in Native English Accent Variation: "Listener Accent, Perceived Accent, and Comprehension"}},
url = {http://ieeexplore.ieee.org/document/1660042/},
volume = {1},
year = {2006}
}
@article{Hanani2013,
abstract = {The paralinguistic information in a speech signal includes clues to the geographical and social background of the speaker. This paper is concerned with automatic extraction of this information from a short segment of speech. A state-of-the-art language identification (LID) system is applied to the problems of regional accent recognition for British English, and ethnic group recognition within a particular accent. We compare the results with human performance and, for accent recognition, the 'text dependent' ACCDIST accent recognition measure. For the 14 regional accents of British English in the ABI-1 corpus (good quality read speech), our LID system achieves a recognition accuracy of 89.6, compared with 95.18 for our best ACCDIST-based system and 58.24 for human listeners. The "Voices across Birmingham" corpus contains significant amounts of telephone conversational speech for the two largest ethnic groups in the city of Birmingham (UK), namely the 'Asian' and 'White' communities. Our LID system distinguishes between these two groups with an accuracy of 96.51 compared with 90.24 for human listeners. Although direct comparison is difficult, it seems that our LID system performs much better on the standard 12 class NIST 2003 Language Recognition Evaluation task or the two class ethnic group recognition task than on the 14 class regional accent recognition task. We conclude that automatic accent recognition is a challenging task for speech technology, and speculate that the use of natural conversational speech may be advantageous for these types of paralinguistic task. {\textcopyright} 2012 Elsevier Ltd. All rights reserved.},
author = {Hanani, A. and Russell, M. J. and Carey, M. J.},
doi = {10.1016/j.csl.2012.01.003},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hanani, Russell, Carey - 2013 - Human and computer recognition of regional accents and ethnic groups from British English speech.pdf:pdf},
isbn = {19909772 (ISSN)},
issn = {08852308},
journal = {Computer Speech and Language},
keywords = {Accent identification,British English,Gaussian Mixture Model,Language identification,Support Vector Machine},
number = {1},
pages = {59--74},
publisher = {Elsevier Ltd},
title = {{Human and computer recognition of regional accents and ethnic groups from British English speech}},
url = {http://dx.doi.org/10.1016/j.csl.2012.01.003},
volume = {27},
year = {2013}
}
@article{Zheng2007,
abstract = {This letter describes a speaker verification system that uses complementary acoustic features derived from the vocal source excitation and the vocal tract system. A new feature set, named the wavelet octave coefficients of residues (WOCOR), is proposed to capture the spectro-temporal source excitation characteristics embedded in the linear predictive residual signal. WOCOR is used to supplement the conventional vocal tract-related features, in this case, the Mel-frequency cepstral coefficients (MFCC), for speaker verification. A novel confidence measure-based score fusion technique is applied to integrate WOCOR and MFCC. Speaker verification experiments are carried out on the NIST 2001 database. The equal error rate (EER) attained with the proposed method is 7.67{\%}, in comparison to 9.30{\%} of the conventional MFCC-based system},
author = {Zheng, Nengheng and Lee, Tan and Ching, P. C.},
doi = {10.1109/LSP.2006.884031},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Zheng, Lee, Ching - 2007 - Integration of complementary acoustic features for speaker recognition.pdf:pdf},
issn = {10709908},
journal = {IEEE Signal Processing Letters},
keywords = {Confidence measure,Information fusion,LP residual signal,Speaker verification,Wavelet transform},
number = {3},
pages = {181--184},
title = {{Integration of complementary acoustic features for speaker recognition}},
volume = {14},
year = {2007}
}
@article{Ghaffari2013,
abstract = {This paper proposes a robust predictive control approach for additive discrete time uncertain nonlinear systems. The controller design is characterized as an optimization problem of the “worst-case” objective function over an infinite moving horizon. A sufficient state feedback synthesis condition is provided in the form of a linear matrix inequality (LMI) optimization and is solved online at each time step. A few simulation examples are exploited to illustrate the effectiveness of this method. Among them are two typical CSTR problems.},
author = {Ghaffari, V. and Naghavi, S. Vahid and a.a. Safavi},
doi = {10.1016/j.jprocont.2013.01.009},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ghaffari, Naghavi, Safavi - 2013 - Robust model predictive control of a class of uncertain nonlinear systems with application to typical.pdf:pdf},
issn = {09591524},
journal = {Journal of Process Control},
number = {4},
pages = {493--499},
publisher = {Elsevier Ltd},
title = {{Robust model predictive control of a class of uncertain nonlinear systems with application to typical CSTR problems}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S095915241300019X},
volume = {23},
year = {2013}
}
@article{Nisioi2015,
abstract = {In this study we investigate the role of different features for the task of native language identification. For this purpose, we compile a learner corpus based on a subset of the EF Cambridge Open Language Database - EFCAMDAT [10] developed at the University of Cambridge in collaboration with EF Education. The features we are taking into con- sideration include character n-grams, positional token frequencies, part of speech n-grams, function words, shell nouns and a set of annotated errors. Last but not least, we examine whether the essays of English learners that share the same mother tongue can be distinguished based on their country of origin.},
author = {Nisioi, Sergiu},
doi = {10.1007/978-3-319-18111-0_49},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Nisioi - 2015 - Feature analysis for native language identification.pdf:pdf},
isbn = {9783319181103},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {644--657},
title = {{Feature analysis for native language identification}},
volume = {9041},
year = {2015}
}
@article{Shriberg2008,
abstract = {Detecting whether a talker is speaking his native language is useful for speaker recognition, speech recognition, and intelligence applications. We study the problem of de-tecting nonnative speakers of American English, using two standard speech corpora. We apply approaches effec-tive in speaker verification to this task, including systems based on MLLR, phone N-gram, prosodic, and word N-gram features. Results show equal error rates between 12{\%} and 20{\%}, depending on the system, test data, and choice of training data. Asymmetries in performance are most likely explained by differences in native language distributions in the corpora. Model combination yields substantial improvements over individual models, with the best result being around 8.6{\%} EER. While phone N-grams are widely used in related tasks (e.g., language and dialect ID), we find that it is the least effective model in combination; MLLR, prosody, and word N-gram systems play stronger roles. Overall, results suggest that individ-ual systems and system combinations found useful for speaker ID also offer promise for nonnativeness detec-tion, and that further efforts are warranted in this area.},
author = {Shriberg, E and Ferrer, L and Kajarekar, S and Scheffer, N and Stolcke, A and Akbacak, M},
file = {:C$\backslash$:/Users/Rajasekhara Reddy/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Shriberg et al. - 2008 - Detecting nonnative speech using speaker recognition approaches.pdf:pdf},
journal = {Proc. IEEE Odyssey Speaker and Language Recognition Workshop},
pages = {1--7},
title = {{Detecting nonnative speech using speaker recognition approaches}},
year = {2008}
}

@InProceedings{Guntur2018,
  author    = {Radha Krishna Guntur and R Krishnan and V.K. Mittal},
  title     = {{Prosodic Analysis of Non-Native South Indian English Speech}},
  booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages},
  year      = {2018},
  pages     = {71--75},
  doi       = {10.21437/SLTU.2018-15},
  url       = {http://dx.doi.org/10.21437/SLTU.2018-15},
}

@InProceedings{Cheng2013,
  author    = {Jian Cheng and Nikhil Bojja and Xin Chen},
  title     = {Automatic accent quantification of indian speakers of English},
  booktitle = {INTERSPEECH},
  year      = {2013},
}

@Article{Reynolds1995,
  author   = {D. A. {Reynolds} and R. C. {Rose}},
  title    = {Robust text-independent speaker identification using Gaussian mixture speaker models},
  journal  = {IEEE Transactions on Speech and Audio Processing},
  year     = {1995},
  volume   = {3},
  number   = {1},
  pages    = {72-83},
  month    = {Jan},
  issn     = {1063-6676},
  doi      = {10.1109/89.365379},
  keywords = {speaker recognition;Gaussian processes;speech processing;telephony;robust text-independent speaker identification;Gaussian mixture speaker models;speaker-dependent spectral shapes;modeling;high identification rates;short utterance;unconstrained conversational speech;degradations;transmission;telephone channel;conversational telephone speech database;algorithmic issues;initialization;variance limiting;model order selection;spectral variability robustness techniques;large population performance;identification accuracy;uni-modal Gaussian technique;VQ codebook;tied Gaussian mixture;radial basis functions;Robustness;Telephony;Speech analysis;Speaker recognition;Spectral shape;Databases;Data mining;Loudspeakers;Degradation},
}

@InProceedings{Doddington2001,
  author    = {George R. Doddington},
  title     = {Speaker recognition based on idiolectal differences between speakers},
  booktitle = {INTERSPEECH},
  year      = {2001},
}

@InBook{Harper2008,
  pages     = {797--810},
  title     = {Spoken Language Characterization},
  publisher = {Springer Berlin Heidelberg},
  year      = {2008},
  author    = {Harper, Mary P. and Maxwell, Michael},
  editor    = {Benesty, Jacob and Sondhi, M. Mohan and Huang, Yiteng Arden},
  address   = {Berlin, Heidelberg},
  isbn      = {978-3-540-49127-9},
  abstract  = {This chapter describes the types of information that can be used spoken-languagecharacterizationto characterize spoken languages. Automatic spoken language identification languageidentification (LID)(LID) systems, which are tasked with determining the identity of the language of speech samples, can utilize a variety of information sources in order to distinguish among languages. In this chapter, we first define what we mean by a language (as opposed to a dialect). We then describe some of the language collections that have been used to investigate spoken language identification, followed by discussion of the types of features that have been or could be utilized by automatic systems and people. In general, approaches used by people and machines differ, perhaps sufficiently to suggest building a partnership between human and machine. We finish with a discussion of the conditions under which textual materials could be used to augment our ability to characterize a spoken language.},
  booktitle = {Springer Handbook of Speech Processing},
  doi       = {10.1007/978-3-540-49127-9_40},
  url       = {https://doi.org/10.1007/978-3-540-49127-9_40},
}

@InProceedings{Krishna2014,
  author    = {Krishna, G. Radha and Krishnan, R.},
  title     = {Influence of Mother Tongue on English Accent},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Processing},
  year      = {2014},
  pages     = {63--67},
  address   = {Goa, India},
  month     = {December},
  publisher = {NLP Association of India},
  url       = {http://www.aclweb.org/anthology/W/W14/W14-5109},
}

@Article{Eady1986,
  author  = {Eady,Stephen J. and Cooper,William E.},
  title   = {Speech intonation and focus location in matched statements and questions},
  journal = {The Journal of the Acoustical Society of America},
  year    = {1986},
  volume  = {80},
  number  = {2},
  pages   = {402-415},
  doi     = {10.1121/1.394091},
  eprint  = {https://doi.org/10.1121/1.394091},
  url     = { 
        https://doi.org/10.1121/1.394091
    
},
}

@InProceedings{Tepperman2008,
  author    = {Joseph Tepperman and Shrikanth Narayanan},
  title     = {Better nonnative intonation scores through prosodic theory},
  booktitle = {INTERSPEECH},
  year      = {2008},
}

@Article{MALMASI2017,
  author    = {MALMASI, SHERVIN and DRAS, MARK},
  title     = {Multilingual native language identification},
  journal   = {Natural Language Engineering},
  year      = {2017},
  volume    = {23},
  number    = {2},
  pages     = {163–215},
  doi       = {10.1017/S1351324915000406},
  publisher = {Cambridge University Press},
}

@InProceedings{Mittal2014a,
  author    = {Vinay Kumar Mittal and Bayya Yegnanarayana},
  title     = {Significance of aperiodicity in the pitch perception of expressive voices},
  booktitle = {INTERSPEECH},
  year      = {2014},
}

@Article{Day2007,
  author   = {P. {Day} and A. K. {Nandi}},
  title    = {Robust Text-Independent Speaker Verification Using Genetic Programming},
  journal  = {IEEE Transactions on Audio, Speech, and Language Processing},
  year     = {2007},
  volume   = {15},
  number   = {1},
  pages    = {285-295},
  month    = {Jan},
  issn     = {1558-7916},
  doi      = {10.1109/TASL.2006.876765},
  keywords = {feature extraction;genetic algorithms;speaker recognition;telephone networks;robust text-independent speaker verification;genetic programming;remote security verification;feature selection;telephone network;additive noise;convolutive noise;Robustness;Genetic programming;Automatic speech recognition;Security;Telephony;Additive noise;Artificial intelligence;Support vector machines;Artificial neural networks;Banking;Genetic programming (GP);speaker verification},
}

@InProceedings{Monnin2010,
  author    = {Julia Monnin and H{\'e}l{\`e}ne Loevenbruck},
  title     = {Language-specific influence on phoneme development: French and drehu data},
  booktitle = {INTERSPEECH},
  year      = {2010},
}

@InProceedings{Luengo2008,
  author    = {Luengo, Iker and Navas, Eva and Sainz, I{\~n}aki and Saratxaga, Ibon and Sanchez, Jon and Odriozola, Igor and Hernaez, Inma},
  title     = {Text Independent Speaker Identification in Multilingual Environments},
  booktitle = {LREC 2008},
  year      = {2008},
  url       = {http://www.lrec-conf.org/proceedings/lrec2008/pdf/461_paper.pdf},
}

@Article{Hermansky1990,
  author  = {Hermansky,Hynek},
  title   = {Perceptual linear predictive (PLP) analysis of speech},
  journal = {The Journal of the Acoustical Society of America},
  year    = {1990},
  volume  = {87},
  number  = {4},
  pages   = {1738-1752},
  doi     = {10.1121/1.399423},
  eprint  = {https://doi.org/10.1121/1.399423},
  url     = { 
        https://doi.org/10.1121/1.399423
    
},
}

@Article{Mittal2013,
  author  = {Mittal,V. K. and Yegnanarayana,B.},
  title   = {Effect of glottal dynamics in the production of shouted speech},
  journal = {The Journal of the Acoustical Society of America},
  year    = {2013},
  volume  = {133},
  number  = {5},
  pages   = {3050-3061},
  doi     = {10.1121/1.4796110},
  eprint  = {https://doi.org/10.1121/1.4796110},
  url     = { 
        https://doi.org/10.1121/1.4796110
    
},
}

@InProceedings{Koreman2018,
  author    = {Jacques Koreman},
  title     = {Category Similarity in Multilingual Pronunciation Training},
  booktitle = {Proc. Interspeech 2018},
  year      = {2018},
  pages     = {2578--2582},
  doi       = {10.21437/Interspeech.2018-1938},
  url       = {http://dx.doi.org/10.21437/Interspeech.2018-1938},
}

@InProceedings{Qian2017,
  author    = {Yao Qian and Keelan Evanini and Xinhao Wang and David Suendermann-Oeft and Robert A. Pugh and Patrick L. Lange and Hillary R. Molloy and Frank K. Soong},
  title     = {Improving Sub-Phone Modeling for Better Native Language Identification with Non-Native English Speech},
  booktitle = {INTERSPEECH},
  year      = {2017},
}

@Article{Kinnunen2010,
  author   = {Tomi Kinnunen and Haizhou Li},
  title    = {An overview of text-independent speaker recognition: From features to supervectors},
  journal  = {Speech Communication},
  year     = {2010},
  volume   = {52},
  number   = {1},
  pages    = {12 - 40},
  issn     = {0167-6393},
  abstract = {This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods. We start with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling. We elaborate advanced computational techniques to address robustness and session variability. The recent progress from vectors towards supervectors opens up a new area of exploration and represents a technology trend. We also provide an overview of this recent development and discuss the evaluation methodology of speaker recognition systems. We conclude the paper with discussion on future directions.},
  doi      = {https://doi.org/10.1016/j.specom.2009.08.009},
  keywords = {Speaker recognition, Text-independence, Feature extraction, Statistical models, Discriminative models, Supervectors, Intersession variability compensation},
  url      = {http://www.sciencedirect.com/science/article/pii/S0167639309001289},
}

@InProceedings{G.RadhaKrishna2018,
  author    = {G.Radha Krishna, R. Krishnan and Vinay Kumar Mittal},
  title     = {{Native Language Identification from South Indian English Speech}},
  booktitle = {Workshop on Machine Learning in Speech and Language Processing, September 7th, 2018},
  year      = {2018},
}

@InProceedings{Krishna2019,
  author    = {G.Radha Krishna and R.Krishnan and V.K.Mittal},
  title     = {An Automated System for Regional Nativity Identification of Indian Speakers from English Speech},
  booktitle = {16th IEEE India Council International Conference INDICON 2019 (Accepted)},
  year      = {2019},
}

@InProceedings{Saha2015,
  author    = {Shambhu Nath Saha and Shyamal Kr. Das Mandal},
  title     = {Study of acoustic correlates of English lexical stress produced by native (L1) bengali speakers compared to native (L1) English speakers},
  booktitle = {INTERSPEECH},
  year      = {2015},
}

@Book{Gussenhoven2004,
  title      = {The Phonology of Tone and Intonation},
  publisher  = {Cambridge University Press},
  year       = {2004},
  author     = {Gussenhoven, Carlos},
  series     = {Research Surveys in Linguistics},
  collection = {Research Surveys in Linguistics},
  doi        = {10.1017/CBO9780511616983},
  place      = {Cambridge},
}

@Article{Bhattacharjee2012,
  author    = {Bhattacharjee, Utpal and Sarmah, Kshirod},
  title     = {GMM-UBM based speaker verification in multilingual environments},
  journal   = {International Journal of Computer Science Issues (IJCSI)},
  year      = {2012},
  volume    = {9},
  number    = {6},
  pages     = {373},
  publisher = {International Journal of Computer Science Issues (IJCSI)},
}

@InProceedings{Campbell2006,
  author    = {W. M. {Campbell} and D. E. {Sturim} and D. A. {Reynolds} and A. {Solomonoff}},
  title     = {SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation},
  booktitle = {2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings},
  year      = {2006},
  volume    = {1},
  pages     = {I-I},
  month     = {May},
  doi       = {10.1109/ICASSP.2006.1659966},
  keywords  = {Gaussian processes;speaker recognition;support vector machines;SVM;speaker verification;NAP variability compensation;GMM supervector kernel;Gaussian mixture models;speaker recognition;support vector machines;nuisance attribute projection;Support vector machines;Kernel;Support vector machine classification;Speaker recognition;Loudspeakers;NIST;Laboratories;Stacking;Acoustic signal detection;Maximum likelihood detection},
}

@Article{Kumar2015,
  author   = {V. Ravi Kumar and Hari Krishna Vydana and Anil Kumar Vuppala},
  title    = {Significance of GMM-UBM based Modelling for Indian Language Identification},
  journal  = {Procedia Computer Science},
  year     = {2015},
  volume   = {54},
  pages    = {231 - 236},
  issn     = {1877-0509},
  note     = {Eleventh International Conference on Communication Networks, ICCN 2015, August 21-23, 2015, Bangalore, India Eleventh International Conference on Data Mining and Warehousing, ICDMW 2015, August 21-23, 2015, Bangalore, India Eleventh International Conference on Image and Signal Processing, ICISP 2015, August 21-23, 2015, Bangalore, India},
  abstract = {Most of the Indian languages are originated from Devanagari, the script of the Sanskrit language. In-spite of similarity in phoneme sets, every language its own influence on the phonotactic constraints of speech in that language. A modelling technique that is capable of capturing the slightest variations imparted by the language is a pre-requisite for developing a language identification system (LID). Use of Gaussian mixture modelling technique with a large number of mixture components demands a large training data for each language class, which is hard to collect and handle. In this work, phonotactic variations imparted by the different languages are modelled using Gaussian mixture modelling with a universal background model (GMM-UBM) technique. In GMM-UBM based modelling certain amount of data from all the language classes is pooled to develop a universal background model (UBM) and the model is adapted to each class. Spectral features (MFCC) are employed to represent the language specific phonotactic information of speech in different languages. During the present study, LID systems are developed using the speech samples from IITKGP-MLILSC. In this work, performance of the proposed GMM-UBM based LID system is compared with conventional GMM based LID system. An average improvement of 7–8% is observed due to the use of UBM-based modelling of developing a LID system.},
  doi      = {https://doi.org/10.1016/j.procs.2015.06.027},
  keywords = {Language identification, Phonotactics, Spectral features, Gaussian mixture modelling (GMM), Universal background model (UBM).},
  url      = {http://www.sciencedirect.com/science/article/pii/S1877050915013514},
}

@Article{mittal2014,
  author    = {Mittal, Vinay Kumar and Yegnanarayana, B and Bhaskararao, Peri},
  title     = {Study of the effects of vocal tract constriction on glottal vibration},
  journal   = {The Journal of the Acoustical Society of America},
  year      = {2014},
  volume    = {136},
  number    = {4},
  pages     = {1932--1941},
  publisher = {ASA},
}

@Article{,
}

@Article{mittal2015,
  author    = {Mittal, Vinay Kumar and Yegnanarayana, B},
  title     = {Study of characteristics of aperiodicity in Noh voices},
  journal   = {The Journal of the Acoustical Society of America},
  year      = {2015},
  volume    = {137},
  number    = {6},
  pages     = {3411--3421},
  publisher = {ASA},
}

@Comment{jabref-meta: databaseType:bibtex;}
