Papers by Yannick Estève
A Multimodal Educational Corpus of Oral Courses: Annotation, Analysis and Case Study (2020.lrec-1)
Copied to clipboard
Salima Mdhaffar, Yannick Estève, Antoine Laurent, Nicolas Hernandez, Richard Dufour, Delphine Charlet, Geraldine Damnati, Solen Quiniou, Nathalie Camelin
| Challenge: | a corpus of spontaneous speech is being developed for educational use . the dataset will be freely available to the research community . |
| Approach: | They propose to use a French speech educational corpus to explore synchronous speech transcription and application in teaching situations. |
| Outcome: | The proposed corpus includes 10 hours of lectures, manually transcribed and segmented . the dataset will be freely available to the research community . |
Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation (2020.lrec-1)
Copied to clipboard
| Challenge: | Summarizing text is not a straightforward task. |
| Approach: | They propose to use automated transcriptions to generate reports from automatic transcriptions as a dataset for neural summarization. |
| Outcome: | The proposed model improves on publicmeetings corpus on a dataset of aligned public meetings. |
Speech Resources in the Tamasheq Language (2022.lrec-1)
Copied to clipboard
Marcely Zanon Boito, Fethi Bougares, Florentin Barbier, Souhir Gahbiche, Loïc Barrault, Mickael Rouvier, Yannick Estève
| Challenge: | In this paper, we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger . we share unlabeled audio data in five languages: french, Fulfulde, Hausa, Tamaheq and Zarma . |
| Approach: | They present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger. |
| Outcome: | The proposed datasets are used in the IWSLT 2022 low-resource speech translation track . they consist of radio recordings from daily broadcast news in Niger and Mali . |
Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models (L18-1)
Copied to clipboard
| Challenge: | Existing speaker adaptation algorithms for BLSTM-CTC AMs are lacking . TED-LIUM corpus shows speaker adaptation provides 11-20% word error rate reduction over baseline model built on raw filter-bank features. |
| Approach: | They propose to use feature-space adaptation techniques for bidirectional long short term memory (BLSTM) recurrent neural network based acoustic models trained with the connectionist temporal classification objective function to improve speaker adaptation. |
| Outcome: | The proposed approach provides up to 11-20% of word error reduction over baseline models on the TED-LIUM corpus. |
The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools (2022.lrec-1)
Copied to clipboard
Gaëlle Laperrière, Valentin Pelloin, Antoine Caubrière, Salima Mdhaffar, Nathalie Camelin, Sahar Ghannay, Bassam Jabaian, Yannick Estève
| Challenge: | a growing number of studies address the spoken language understanding domain through a simple task like speech intent detection. |
| Approach: | They focus on the french MEDIA SLU dataset, which is distributed since 2005 . they propose a recipe for its use, including data preparation, training and evaluation scripts . |
| Outcome: | The MEDIA SLU dataset is used as a benchmark dataset for a large number of research projects. |
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding (2022.lrec-1)
Copied to clipboard
Salima Mdhaffar, Valentin Pelloin, Antoine Caubrière, Gaëlle Laperriere, Sahar Ghannay, Bassam Jabaian, Nathalie Camelin, Yannick Estève
| Challenge: | Pretrained models have been introduced for both acoustic and language modeling. |
| Approach: | They present an error analysis of pretrained models using a french MEDIA benchmark dataset. |
| Outcome: | The proposed models have been able to improve on the french MEDIA benchmark dataset, which is one of the most challenging among all benchmarks accessible to the entire research community. |
TARIC-SLU: A Tunisian Benchmark Dataset for Spoken Language Understanding (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing SLU resources are limited in high-resource languages such as English, Mandarin and French. |
| Approach: | They propose to use a Tunisian dialect dataset to build a semantic model of the system that is continuously annotated with dialogue acts and slots. |
| Outcome: | The proposed dataset is based on train-based and ASR-based models of train-driven conversations in Tunisian dialect. |
Where are we in Named Entity Recognition from Speech? (2020.lrec-1)
Copied to clipboard
| Challenge: | Named entity recognition is usually made through a pipeline process that consists of processing audio and applying a NER to the audio outputs. |
| Approach: | They propose an original 3-pass approach and explore the capability of an E2E system to do structured NER. |
| Outcome: | The proposed system performs better than the current pipeline approach. |
AlloSat: A New Call Center French Corpus for Satisfaction and Frustration Analysis (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing systems retrieve emotional information from textual transcriptions or from audio signal. |
| Approach: | They propose to use a call center corpus that is continuously annotated in frustration and satisfaction to model the continuous aspect of semantic and paralinguistic information at the conversation level. |
| Outcome: | The proposed system can model the paralinguistic aspect of semantic and paralinguistic information at the conversation level. |
Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants (2024.lrec-main)
Copied to clipboard
Chloe Sekkat, Fanny Leroy, Salima Mdhaffar, Blake Perry Smith, Yannick Estève, Joseph Dureau, Alice Coucke
| Challenge: | Recent studies show voice assistants do not perform equally well for everyone . however, research on demographic robustness of speech technologies is still scarce . |
| Approach: | They propose a statistical method to detect demographic bias using a large dataset with controlled demographic tags. |
| Outcome: | The proposed method shows statistically significant differences in performance across age, dialectal region and ethnicity. |
Simulating ASR errors for training SLU systems (L18-1)
Copied to clipboard
| Challenge: | Existing methods to simulate automatic speech recognition errors from manual transcriptions are not available during training of the SLU model. |
| Approach: | They propose to use acoustic and linguistic word embeddings to define a similarity measure between words to predict ASR confusions. |
| Outcome: | The proposed method significantly improves the performance of spoken language understanding systems. |
Toward Qualitative Evaluation of Embeddings for Arabic Sentiment Analysis (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing studies on Arabic sentiment analysis (SA) tasks focus on word embeddings to capture semantic and syntactic similarities, but Arabic language is characterized by its agglutination and morphological richness contributing to great sparsity. |
| Approach: | They propose several protocols to evaluate specific embeddings for Arabic sentiment analysis task. |
| Outcome: | The proposed embeddings are based on words and lemmas in Arabic sentiment analysis (SA) task. |
FrNewsLink : a corpus linking TV Broadcast News Segments and Press Articles (L18-1)
Copied to clipboard
Nathalie Camelin, Géraldine Damnati, Abdessalam Bouchekif, Anais Landeau, Delphine Charlet, Yannick Estève
| Challenge: | a corpus of TV Broadcast News resources is proposed to address several applicative tasks. |
| Approach: | They propose to use a corpus to address several applicative tasks that are made public . they propose to gather TVBN shows and press articles and use them to study semantic similarity . |
| Outcome: | The proposed corpus is based on 112 TVBN shows and press articles . it allows to study semantic similarity and multimedia News linking . |