Dominique Fohr


2020

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Reconnaissance automatique de la parole : génération des prononciations non natives pour l’enrichissement du lexique (In this study we propose a method for lexicon adaptation in order to improve the automatic speech recognition (ASR) of non-native speakers)
Ismael Bada | Dominique Fohr | Irina Illina
Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la Parole

Dans cet article nous proposons une méthode d’adaptation du lexique, destinée à améliorer les systèmes de la reconnaissance automatique de la parole (SRAP) des locuteurs non natifs. En effet, la reconnaissance automatique souffre d’une chute significative de ses performances quand elle est utilisée pour reconnaître la parole des locuteurs non natifs, car les phonèmes de la langue étrangère sont fréquemment mal prononcés par ces locuteurs. Pour prendre en compte ce problème de prononciations erronées, notre approche propose d’intégrer les prononciations non natives dans le lexique et par la suite d’utiliser ce lexique enrichi pour la reconnaissance. Pour réaliser notre approche nous avons besoin d’un petit corpus de parole non native et de sa transcription. Pour générer les prononciations non natives, nous proposons de tenir compte des correspondances graphèmes-phonèmes en vue de générer de manière automatique des règles de création de nouvelles prononciations. Ces nouvelles prononciations seront ajoutées au lexique. Nous présentons une évaluation de notre méthode sur un corpus de locuteurs non natifs français s’exprimant en anglais.

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Introduction d’informations sémantiques dans un système de reconnaissance de la parole (Despite spectacular advances in recent years, the Automatic Speech Recognition (ASR) systems still make mistakes, especially in noisy environments)
Stéphane Level | Irina Illina | Dominique Fohr
Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la Parole

Malgré les avancés spectaculaires ces dernières années, les systèmes de Reconnaissance Automatique de Parole (RAP) commettent encore des erreurs, surtout dans des environnements bruités. Pour améliorer la RAP, nous proposons de se diriger vers une contextualisation d’un système RAP, car les informations sémantiques sont importantes pour la performance de la RAP. Les systèmes RAP actuels ne prennent en compte principalement que les informations lexicales et syntaxiques. Pour modéliser les informations sémantiques, nous proposons de détecter les mots de la phrase traitée qui pourraient avoir été mal reconnus et de proposer des mots correspondant mieux au contexte. Cette analyse sémantique permettra de réévaluer les N meilleures hypothèses de transcription (N-best). Nous utilisons les embeddings Word2Vec et BERT. Nous avons évalué notre méthodologie sur le corpus des conférences TED (TED-LIUM). Les résultats montrent une amélioration significative du taux d’erreur mots en utilisant la méthodologie proposée.

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Projet AMIS : résumé et traduction automatique de vidéos (AMIS project : automatic summarization and translation of videos)
Mohamed Amine Menacer | Dominique Fohr | Denis Jouvet | Karima Abidi | David Langlois | Kamel Smaïli
Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 4 : Démonstrations et résumés d'articles internationaux

La démonstration de résumé et de traduction automatique de vidéos résulte de nos travaux dans le projet AMIS. L’objectif du projet était d’aider un voyageur à comprendre les nouvelles dans un pays étranger. Pour cela, le projet propose de résumer et traduire automatiquement une vidéo en langue étrangère (ici, l’arabe). Un autre objectif du projet était aussi de comparer les opinions et sentiments exprimés dans plusieurs vidéos comparables. La démonstration porte sur l’aspect résumé, transcription et traduction. Les exemples montrés permettront de comprendre et mesurer qualitativement les résultats du projet.

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Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification
Ashwin Geet D’Sa | Irina Illina | Dominique Fohr | Dietrich Klakow | Dana Ruiter
Proceedings of the First Workshop on Insights from Negative Results in NLP

Research on hate speech classification has received increased attention. In real-life scenarios, a small amount of labeled hate speech data is available to train a reliable classifier. Semi-supervised learning takes advantage of a small amount of labeled data and a large amount of unlabeled data. In this paper, label propagation-based semi-supervised learning is explored for the task of hate speech classification. The quality of labeling the unlabeled set depends on the input representations. In this work, we show that pre-trained representations are label agnostic, and when used with label propagation yield poor results. Neural network-based fine-tuning can be adopted to learn task-specific representations using a small amount of labeled data. We show that fully fine-tuned representations may not always be the best representations for the label propagation and intermediate representations may perform better in a semi-supervised setup.

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Towards Non-Toxic Landscapes: Automatic Toxic Comment Detection Using DNN
Ashwin Geet D’Sa | Irina Illina | Dominique Fohr
Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying

The spectacular expansion of the Internet has led to the development of a new research problem in the field of natural language processing: automatic toxic comment detection, since many countries prohibit hate speech in public media. There is no clear and formal definition of hate, offensive, toxic and abusive speeches. In this article, we put all these terms under the umbrella of “toxic speech”. The contribution of this paper is the design of binary classification and regression-based approaches aiming to predict whether a comment is toxic or not. We compare different unsupervised word representations and different DNN based classifiers. Moreover, we study the robustness of the proposed approaches to adversarial attacks by adding one (healthy or toxic) word. We evaluate the proposed methodology on the English Wikipedia Detox corpus. Our experiments show that using BERT fine-tuning outperforms feature-based BERT, Mikolov’s and fastText representations with different DNN classifiers.

2017

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An enhanced automatic speech recognition system for Arabic
Mohamed Amine Menacer | Odile Mella | Dominique Fohr | Denis Jouvet | David Langlois | Kamel Smaili
Proceedings of the Third Arabic Natural Language Processing Workshop

Automatic speech recognition for Arabic is a very challenging task. Despite all the classical techniques for Automatic Speech Recognition (ASR), which can be efficiently applied to Arabic speech recognition, it is essential to take into consideration the language specificities to improve the system performance. In this article, we focus on Modern Standard Arabic (MSA) speech recognition. We introduce the challenges related to Arabic language, namely the complex morphology nature of the language and the absence of the short vowels in written text, which leads to several potential vowelization for each graphemes, which is often conflicting. We develop an ASR system for MSA by using Kaldi toolkit. Several acoustic and language models are trained. We obtain a Word Error Rate (WER) of 14.42 for the baseline system and 12.2 relative improvement by rescoring the lattice and by rewriting the output with the right Z hamoza above or below Alif.

2016

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The IFCASL Corpus of French and German Non-native and Native Read Speech
Juergen Trouvain | Anne Bonneau | Vincent Colotte | Camille Fauth | Dominique Fohr | Denis Jouvet | Jeanin Jügler | Yves Laprie | Odile Mella | Bernd Möbius | Frank Zimmerer
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The IFCASL corpus is a French-German bilingual phonetic learner corpus designed, recorded and annotated in a project on individualized feedback in computer-assisted spoken language learning. The motivation for setting up this corpus was that there is no phonetically annotated and segmented corpus for this language pair of comparable of size and coverage. In contrast to most learner corpora, the IFCASL corpus incorporate data for a language pair in both directions, i.e. in our case French learners of German, and German learners of French. In addition, the corpus is complemented by two sub-corpora of native speech by the same speakers. The corpus provides spoken data by about 100 speakers with comparable productions, annotated and segmented on the word and the phone level, with more than 50% manually corrected data. The paper reports on inter-annotator agreement and the optimization of the acoustic models for forced speech-text alignment in exercises for computer-assisted pronunciation training. Example studies based on the corpus data with a phonetic focus include topics such as the realization of /h/ and glottal stop, final devoicing of obstruents, vowel quantity and quality, pitch range, and tempo.

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How Diachronic Text Corpora Affect Context based Retrieval of OOV Proper Names for Audio News
Imran Sheikh | Irina Illina | Dominique Fohr
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Out-Of-Vocabulary (OOV) words missed by Large Vocabulary Continuous Speech Recognition (LVCSR) systems can be recovered with the help of topic and semantic context of the OOV words captured from a diachronic text corpus. In this paper we investigate how the choice of documents for the diachronic text corpora affects the retrieval of OOV Proper Names (PNs) relevant to an audio document. We first present our diachronic French broadcast news datasets, which highlight the motivation of our study on OOV PNs. Then the effect of using diachronic text data from different sources and a different time span is analysed. With OOV PN retrieval experiments on French broadcast news videos, we conclude that a diachronic corpus with text from different sources leads to better retrieval performance than one relying on text from single source or from a longer time span.

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Weakly-supervised text-to-speech alignment confidence measure
Guillaume Serrière | Christophe Cerisara | Dominique Fohr | Odile Mella
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This work proposes a new confidence measure for evaluating text-to-speech alignment systems outputs, which is a key component for many applications, such as semi-automatic corpus anonymization, lips syncing, film dubbing, corpus preparation for speech synthesis and speech recognition acoustic models training. This confidence measure exploits deep neural networks that are trained on large corpora without direct supervision. It is evaluated on an open-source spontaneous speech corpus and outperforms a confidence score derived from a state-of-the-art text-to-speech aligner. We further show that this confidence measure can be used to fine-tune the output of this aligner and improve the quality of the resulting alignment.

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Learning Word Importance with the Neural Bag-of-Words Model
Imran Sheikh | Irina Illina | Dominique Fohr | Georges Linarès
Proceedings of the 1st Workshop on Representation Learning for NLP

2014

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Designing a Bilingual Speech Corpus for French and German Language Learners: a Two-Step Process
Camille Fauth | Anne Bonneau | Frank Zimmerer | Juergen Trouvain | Bistra Andreeva | Vincent Colotte | Dominique Fohr | Denis Jouvet | Jeanin Jügler | Yves Laprie | Odile Mella | Bernd Möbius
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present the design of a corpus of native and non-native speech for the language pair French-German, with a special emphasis on phonetic and prosodic aspects. To our knowledge there is no suitable corpus, in terms of size and coverage, currently available for the target language pair. To select the target L1-L2 interference phenomena we prepare a small preliminary corpus (corpus1), which is analyzed for coverage and cross-checked jointly by French and German experts. Based on this analysis, target phenomena on the phonetic and phonological level are selected on the basis of the expected degree of deviation from the native performance and the frequency of occurrence. 14 speakers performed both L2 (either French or German) and L1 material (either German or French). This allowed us to test, recordings duration, recordings material, the performance of our automatic aligner software. Then, we built corpus2 taking into account what we learned about corpus1. The aims are the same but we adapted speech material to avoid too long recording sessions. 100 speakers will be recorded. The corpus (corpus1 and corpus2) will be prepared as a searchable database, available for the scientific community after completion of the project.

2012

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Détection de transcriptions incorrectes de parole non-native dans le cadre de l’apprentissage de langues étrangères (Detection of incorrect transcriptions of non-native speech in the context of foreign language learning) [in French]
Luiza Orosanu | Denis Jouvet | Dominique Fohr | Irina Illina | Anne Bonneau
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 1: JEP

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Génération des prononciations de noms propres à l’aide des Champs Aléatoires Conditionnels (Pronunciation generation for proper names using Conditional Random Fields) [in French]
Irina Illina | Dominique Fohr | Denis Jouvet
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 1: JEP

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CoALT: A Software for Comparing Automatic Labelling Tools
Dominique Fohr | Odile Mella
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Speech-text alignment tools are frequently used in speech technology and research. In this paper, we propose a GPL software CoALT (Comparing Automatic Labelling Tools) for comparing two automatic labellers or two speech-text alignment tools, ranking them and displaying statistics about their differences. The main feature of CoALT is that a user can define its own criteria for evaluating and comparing the speech-text alignment tools since the required quality for labelling depends on the targeted application. Beyond ranking, our tool provides useful statistics for each labeller and above all about their differences and can emphasize the drawbacks and advantages of each labeller. We have applied our software for the French and English languages but it can be used for another language by simply defining the list of the phonetic symbols and optionally a set of phonetic rules. In this paper we present the usage of the software for comparing two automatic labellers on the corpus TIMIT. Moreover, as automatic labelling tools are configurable (number of GMMs, phonetic lexicon, acoustic parameterisation), we then present how CoALT allows to determine the best parameters for our automatic labelling tool.

2004

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A Complete Understanding Speech System Based on Semantic Concepts
Salma Jamoussi | Kamel Smaïli | Dominique Fohr | Jean-Paul Haton
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Development of New Telephone Speech Databases for French: the NEOLOGOS Project
Elisabeth Pinto | Delphine Charlet | Hélène François | Djamel Mostefa | Olivier Boëffard | Dominique Fohr | Odile Mella | Frédéric Bimbot | Khalid Choukri | Yann Philip | Francis Charpentier
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)