Sign language-to-text translation systems are similar to spoken language translation systems in that they consist of a recognition phase and a translation phase. First, the video of a person signing is transformed into a transcription of the signs, which is then translated into the text of a spoken language. One distinctive feature of sign languages is their multi-modal nature, as they can express meaning simultaneously via hand movements, body posture and facial expressions. In some sign languages, certain signs are accompanied by mouthings, i.e. the person silently pronounces the word while signing. In this work, we closely integrate a recognition and translation framework by adding a viseme recognizer (“lip reading system”) based on an active appearance model and by optimizing the recognition system to improve the translation output. The system outperforms the standard approach of separate recognition and translation.

In this paper, we introduce a new parallel corpus of subtitles of educational videos: the AMARA corpus for online educational content. We crawl a multilingual collection community generated subtitles, and present the results of processing the Arabic–English portion of the data, which yields a parallel corpus of about 2.6M Arabic and 3.9M English words. We explore different approaches to align the segments, and extrinsically evaluate the resulting parallel corpus on the standard TED-talks tst-2010. We observe that the data can be successfully used for this task, and also observe an absolute improvement of 1.6 BLEU when it is used in combination with TED data. Finally, we analyze some of the specific challenges when translating the educational content.

There has been a fair amount of work on automatic speech translation systems that translate in real-time, serving as a computerized version of a simultaneous interpreter. It has been noticed in the field of translation studies that simultaneous interpreters perform a number of tricks to make the content easier to understand in real-time, including dividing their translations into small chunks, or summarizing less important content. However, the majority of previous work has not specifically considered this fact, simply using translation data (made by translators) for learning of the machine translation system. In this paper, we examine the possibilities of additionally incorporating simultaneous interpretation data (made by simultaneous interpreters) in the learning process. First we collect simultaneous interpretation data from professional simultaneous interpreters of three levels, and perform an analysis of the data. Next, we incorporate the simultaneous interpretation data in the learning of the machine translation system. As a result, the translation style of the system becomes more similar to that of a highly experienced simultaneous interpreter. We also find that according to automatic evaluation metrics, our system achieves performance similar to that of a simultaneous interpreter that has 1 year of experience.

We investigate the problem of combining the outputs of different translation systems into a minimum Bayes’ risk consensus translation. We explore different risk formulations based on the BLEU score, and provide a dynamic programming decoding algorithm for each of them. In our experiments, these algorithms generated consensus translations with better risk, and more efficiently, than previous proposals.

We present a method to estimate the quality of automatic translations when reference translations are not available. Quality estimation is addressed as a two-step regression problem where multiple features are combined to predict a quality score. Given a set of features, we aim at automatically extracting the variables that better explain translation quality, and use them to predict the quality score. The soundness of our approach is assessed by the encouraging results obtained in an exhaustive experimentation with several feature sets. Moreover, the studied approach is highly-scalable allowing us to employ hundreds of features to predict translation quality.

This paper describes our Speech-to-Text (STT) system for French, which was developed as part of our efforts in the Quaero program for the 2013 evaluation. Our STT system consists of six subsystems which were created by combining multiple complementary sources of pronunciation modeling including graphemes with various feature front-ends based on deep neural networks and tonal features. Both speaker-independent and speaker adaptively trained versions of the systems were built. The resulting systems were then combined via confusion network combination and crossadaptation. Through progressive advances and system combination we reach a word error rate (WER) of 16.5% on the 2012 Quaero evaluation data.

In this article, we present a sampling-based approach to improve bilingual sub-sentential alignment in parallel corpora. This approach can be used to align parallel sentences on an as needed basis, and is able to accurately align newly available sentences. We evaluate the resulting alignments on several Machine Translation tasks. Results show that for the tasks considered here, our approach performs on par with the state-of-the-art statistical alignment pipeline giza++/Moses, and obtains superior results in a number of configurations, notably when aligning additional parallel sentence pairs carefully selected to match the test input.

In this paper we describe our work on unsupervised adaptation of the acoustic model of our simultaneous lecture translation system. We trained a speaker independent acoustic model, with which we produce automatic transcriptions of new lectures in order to improve the system for a specific lecturer. We compare our results against a model that was trained in a supervised way on an exact manual transcription. We examine four different ways of processing the decoder outputs of the automatic transcription with respect to the treatment of pronunciation variants and noise words. We will show that, instead of fixating the latter informations in the transcriptions, it is of advantage to let the Viterbi algorithm during training decide which pronunciations to use and where to insert which noise words. Further, we utilize word level posterior probabilities obtained during decoding by weighting and thresholding the words of a transcription.

Current ASR and MT systems do not operate on conversational Finnish, because training data for colloquial Finnish has not been available. Although speech recognition performance on literary Finnish is already quite good, those systems have very poor baseline performance in conversational speech. Text data for relevant vocabulary and language models can be collected from the Internet, but web data is very noisy and most of it is not helpful for learning good models. Finnish language is highly agglutinative, and written phonetically. Even phonetic reductions and sandhi are often written down in informal discussions. This increases vocabulary size dramatically and causes word-based selection methods to fail. Our selection method explicitly optimizes the perplexity of a subword language model on the development data, and requires only very limited amount of speech transcripts as development data. The language models have been evaluated for speech recognition using a new data set consisting of generic colloquial Finnish.

The ability to quickly incorporate incoming training data into a running translation system is critical in a number of applications. Mechanisms based on incremental model update and the online EM algorithm hold the promise of achieving this objective in a principled way. Still, efficient tools for incremental training are yet to be available. In this paper we experiment with simple alternative solutions for interim model updates, within the popular Moses system. Short of updating the model in real time, such updates can execute in short timeframes even when operating on large models, and achieve a performance level close to, and in some cases exceeding, that of batch retraining.

We analyze the performance of source sentence reordering, a common reordering approach, using oracle experiments on German-English and English-German translation. First, we show that the potential of this approach is very promising. Compared to a monotone translation, the optimally reordered source sentence leads to improvements of up to 4.6 and 6.2 BLEU points, depending on the language. Furthermore, we perform a detailed evaluation of the different aspects of the approach. We analyze the impact of the restriction of the search space by reordering lattices and we can show that using more complex rule types for reordering results in better approximation of the optimally reordered source. However, a gap of about 3 to 3.8 BLEU points remains, presenting a promising perspective for research on extending the search space through better reordering rules. When evaluating the ranking of different reordering variants, the results reveal that the search for the best path in the lattice performs very well for German-English translation. For English-German translation there is potential for an improvement of up to 1.4 BLEU points through a better ranking of the different reordering possibilities in the reordering lattice.

Disfluencies in speech pose severe difficulties in machine translation of spontaneous speech. This paper presents our conditional random field (CRF)-based speech disfluency detection system developed on German to improve spoken language translation performance. In order to detect speech disfluencies considering syntactics and semantics of speech utterances, we carried out a CRF-based approach using information learned from the word representation and the phrase table used for machine translation. The word representation is gained using recurrent neural networks and projected words are clustered using the k-means algorithm. Using the output from the model trained with the word representations and phrase table information, we achieve an improvement of 1.96 BLEU points on the lecture test set. By keeping or removing humanannotated disfluencies, we show an upper bound and lower bound of translation quality. In an oracle experiment we gain 3.16 BLEU points of improvement on the lecture test set, compared to the same set with all disfluencies.

Russian is a challenging language for automatic speech recognition systems due to its rich morphology. This rich morphology stems from Russian’s highly inflectional nature and the frequent use of preand suffixes. Also, Russian has a very free word order, changes in which are used to reflect connotations of the sentences. Dealing with these phenomena is rather difficult for traditional n-gram models. We therefore investigate in this paper the use of a maximum entropy language model for Russian whose features are specifically designed to deal with the inflections in Russian, as well as the loose word order. We combine this with a subword based language model in order to alleviate the problem of large vocabulary sizes necessary for dealing with highly inflecting languages. Applying the maximum entropy language model during re-scoring improves the word error rate of our recognition system by 1.2% absolute, while the use of the sub-word based language model reduces the vocabulary size from 120k to 40k and the OOV rate from 4.8% to 2.1%.

Research into the translation of the output of automatic speech recognition (ASR) systems is hindered by the dearth of datasets developed for that explicit purpose. For SpanishEnglish translation, in particular, most parallel data available exists only in vastly different domains and registers. In order to support research on cross-lingual speech applications, we introduce the Fisher and Callhome Spanish-English Speech Translation Corpus, supplementing existing LDC audio and transcripts with (a) ASR 1-best, lattice, and oracle output produced by the Kaldi recognition system and (b) English translations obtained on Amazon’s Mechanical Turk. The result is a four-way parallel dataset of Spanish audio, transcriptions, ASR lattices, and English translations of approximately 38 hours of speech, with defined training, development, and held-out test sets. We conduct baseline machine translation experiments using models trained on the provided training data, and validate the dataset by corroborating a number of known results in the field, including the utility of in-domain (information, conversational) training data, increased performance translating lattices (instead of recognizer 1-best output), and the relationship between word error rate and BLEU score.

We present the first known experiments incorporating unsupervised bilingual nonterminal category learning within end-to-end fully unsupervised transduction grammar induction using matched training and testing models. Despite steady recent progress, such induction experiments until now have not allowed for learning differentiated nonterminal categories. We divide the learning into two stages: (1) a bootstrap stage that generates a large set of categorized short transduction rule hypotheses, and (2) a minimum conditional description length stage that simultaneously prunes away less useful short rule hypotheses, while also iteratively segmenting full sentence pairs into useful longer categorized transduction rules. We show that the second stage works better when the rule hypotheses have categories than when they do not, and that the proposed conditional description length approach combines the rules hypothesized by the two stages better than a mixture model does. We also show that the compact model learned during the second stage can be further improved by combining the result of different iterations in a mixture model. In total, we see a jump in BLEU score, from 17.53 for a standalone minimum description length baseline with no category learning, to 20.93 when incorporating category induction on a Chinese–English translation task.

This paper describes a study of translation hypotheses that can be obtained by iterative, greedy oracle improvement from the best hypothesis of a state-of-the-art phrase-based Statistical Machine Translation system. The factors that we consider include the influence of the rewriting operations, target languages, and training data sizes. Analysis of our results provide new insights into some previously unanswered questions, which include the reachability of previously unreachable hypotheses via indirect translation (thanks to the introduction of a rewrite operation on the source text), and the potential translation performance of systems relying on pruned phrase tables.

Spoken language translation (SLT) systems typically follow a pipeline architecture, in which the best automatic speech recognition (ASR) hypothesis of an input utterance is fed into a statistical machine translation (SMT) system. Conversational speech often generates unrecoverable ASR errors owing to its rich vocabulary (e.g. out-of-vocabulary (OOV) named entities). In this paper, we study the possibility of alleviating the impact of unrecoverable ASR errors on translation performance by minimizing the contextual effects of incorrect source words in target hypotheses. Our approach is driven by locally-derived penalties applied to bilingual phrase pairs as well as target language model (LM) likelihoods in the vicinity of source errors. With oracle word error labels on an OOV word-rich English-to-Iraqi Arabic translation task, we show statistically significant relative improvements of 3.2% BLEU and 2.0% METEOR over an error-agnostic baseline SMT system. We then investigate the impact of imperfect source error labels on error-aware translation performance. Simulation experiments reveal that modest translation improvements are to be gained with this approach even when the source error labels are noisy.

This paper focuses on the user experience (UX) of a simultaneous interpretation system for face-to-face conversation between two users. To assess the UX of the system, we first made a transcript of the speech of users recorded during a task-based evaluation experiment and then analyzed user speech from the viewpoint of UX. In a task-based evaluation experiment, 44 tasks out of 45 tasks were solved. The solved task ratio was 97.8%. This indicates that the system can effectively provide interpretation to enable users to solve tasks. However, we found that users repeated speech due to errors in automatic speech recognition (ASR) or machine translation (MT). Users repeated clauses 1.8 times on average. Users seemed to repeat themselves until they received a response from their partner users. In addition, we found that after approximately 3.6 repetitions, users would change their words to avoid errors in ASR or MT and to evoke a response from their partner users.

In this paper, we apply a set of approaches to, efficiently, rescore the output of the automatic speech recognition over weather-domain data. Since the in-domain data is usually insufficient for training an accurate language model (LM) we utilize an automatic selection method to extract domain-related sentences from a general text resource. Then, an N-gram language model is trained on this set. We exploit this LM, along with a pre-trained acoustic model for recognition of the development and test instances. The recognizer generates a confusion network (CN) for each instance. Afterwards, we make use of the recurrent neural network language model (RNNLM), trained on the in-domain data, in order to iteratively rescore the CNs. Rescoring the CNs, in this way, requires estimating the weights of the RNNLM, N-gramLM and acoustic model scores. Weights optimization is the critical part of this work, whereby, we propose using the minimum error rate training (MERT) algorithm along with a novel N-best list extraction method. The experiments are done over weather forecast domain data that has been provided in the framework of EUBRIDGE project.