Mireia Farrús


Recycle Your Wav2Vec2 Codebook: A Speech Perceiver for Keyword Spotting
Guillermo Cámbara | Jordi Luque | Mireia Farrús
Proceedings of the 29th International Conference on Computational Linguistics

Speech information in a pretrained wav2vec2.0 model is usually leveraged through its encoder, which has at least 95M parameters, being not so suitable for small footprint Keyword Spotting. In this work, we show an efficient way of profiting from wav2vec2.0’s linguistic knowledge, by recycling the phonetic information encoded in its latent codebook, which has been typically thrown away after pretraining. We do so by transferring the codebook as weights for the latent bottleneck of a Keyword Spotting Perceiver, thus initializing such model with phonetic embeddings already. The Perceiver design relies on cross-attention between these embeddings and input data to generate better representations. Our method delivers accuracy gains compared to random initialization, at no latency costs. Plus, we show that the phonetic embeddings can easily be downsampled with k-means clustering, speeding up inference in 3.5 times at only slight accuracy penalties.


Compilation of Corpora for the Study of the Information Structure–Prosody Interface
Alicia Burga | Mónica Domínguez | Mireia Farrús | Leo Wanner
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


Automatic Extraction of Parallel Speech Corpora from Dubbed Movies
Alp Öktem | Mireia Farrús | Leo Wanner
Proceedings of the 10th Workshop on Building and Using Comparable Corpora

This paper presents a methodology to extract parallel speech corpora based on any language pair from dubbed movies, together with an application framework in which some corresponding prosodic parameters are extracted. The obtained parallel corpora are especially suitable for speech-to-speech translation applications when a prosody transfer between source and target languages is desired.


An Automatic Prosody Tagger for Spontaneous Speech
Mónica Domínguez | Mireia Farrús | Leo Wanner
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Speech prosody is known to be central in advanced communication technologies. However, despite the advances of theoretical studies in speech prosody, so far, no large scale prosody annotated resources that would facilitate empirical research and the development of empirical computational approaches are available. This is to a large extent due to the fact that current common prosody annotation conventions offer a descriptive framework of intonation contours and phrasing based on labels. This makes it difficult to reach a satisfactory inter-annotator agreement during the annotation of gold standard annotations and, subsequently, to create consistent large scale annotations. To address this problem, we present an annotation schema for prominence and boundary labeling of prosodic phrases based upon acoustic parameters and a tagger for prosody annotation at the prosodic phrase level. Evaluation proves that inter-annotator agreement reaches satisfactory values, from 0.60 to 0.80 Cohen’s kappa, while the prosody tagger achieves acceptable recall and f-measure figures for five spontaneous samples used in the evaluation of monologue and dialogue formats in English and Spanish. The work presented in this paper is a first step towards a semi-automatic acquisition of large corpora for empirical prosodic analysis.

Praat on the Web: An Upgrade of Praat for Semi-Automatic Speech Annotation
Mónica Domínguez | Iván Latorre | Mireia Farrús | Joan Codina-Filbà | Leo Wanner
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

This paper presents an implementation of the widely used speech analysis tool Praat as a web application with an extended functionality for feature annotation. In particular, Praat on the Web addresses some of the central limitations of the original Praat tool and provides (i) enhanced visualization of annotations in a dedicated window for feature annotation at interval and point segments, (ii) a dynamic scripting composition exemplified with a modular prosody tagger, and (iii) portability and an operational web interface. Speech annotation tools with such a functionality are key for exploring large corpora and designing modular pipelines.


Linguistic-based Evaluation Criteria to identify Statistical Machine Translation Errors
Mireia Farrús | Marta R. Costa-jussà | José B. Mariño | José A. R. Fonollosa
Proceedings of the 14th Annual conference of the European Association for Machine Translation

Automatic and Human Evaluation Study of a Rule-based and a Statistical Catalan-Spanish Machine Translation Systems
Marta R. Costa-jussà | Mireia Farrús | José B. Mariño | José A. R. Fonollosa
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Machine translation systems can be classified into rule-based and corpus-based approaches, in terms of their core technology. Since both paradigms have largely been used during the last years, one of the aims in the research community is to know how these systems differ in terms of translation quality. To this end, this paper reports a study and comparison of a rule-based and a corpus-based (particularly, statistical) Catalan-Spanish machine translation systems, both of them freely available in the web. The translation quality analysis is performed under two different domains: journalistic and medical. The systems are evaluated by using standard automatic measures, as well as by native human evaluators. Automatic results show that the statistical system performs better than the rule-based system. Human judgements show that in the Spanish-to-Catalan direction the statistical system also performs better than the rule-based system, while in the Catalan-to-Spanish direction is the other way round. Although the statistical system obtains the best automatic scores, its errors tend to be more penalized by human judgements than the errors of the rule-based system. This can be explained because statistical errors are usually unexpected and they do not follow any pattern.


Improving a Catalan-Spanish Statistical Translation System using Morphosyntactic Knowledge
Mireia Farrús | Marta R. Costa-jussà | Marc Poch | Adolfo Hernández | José B. Mariño
Proceedings of the 13th Annual conference of the European Association for Machine Translation