Search Results for author: Sahar Ghannay

Found 20 papers, 6 papers with code

Analyzing BERT Cross-lingual Transfer Capabilities in Continual Sequence Labeling

1 code implementation MMMPIE (COLING) 2022 Juan Manuel Coria, Mathilde Veron, Sahar Ghannay, Guillaume Bernard, Hervé Bredin, Olivier Galibert, Sophie Rosset

Knowledge transfer between neural language models is a widely used technique that has proven to improve performance in a multitude of natural language tasks, in particular with the recent rise of large pre-trained language models like BERT.

Continual Learning Cross-Lingual Transfer +6

Benchmarking Transformers-based models on French Spoken Language Understanding tasks

no code implementations19 Jul 2022 Oralie Cattan, Sahar Ghannay, Christophe Servan, Sophie Rosset

In this paper, we propose a unified benchmark, focused on evaluating models quality and their ecological impact on two well-known French spoken language understanding tasks.

Benchmarking Spoken Language Understanding

Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation

1 code implementation14 Sep 2021 Juan M. Coria, Hervé Bredin, Sahar Ghannay, Sophie Rosset

We propose to address online speaker diarization as a combination of incremental clustering and local diarization applied to a rolling buffer updated every 500ms.

speaker-diarization Speaker Diarization

Where are we in semantic concept extraction for Spoken Language Understanding?

no code implementations24 Jun 2021 Sahar Ghannay, Antoine Caubrière, Salima Mdhaffar, Gaëlle Laperrière, Bassam Jabaian, Yannick Estève

More recent works on self-supervised training with unlabeled data open new perspectives in term of performance for automatic speech recognition and natural language processing.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +7

LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis

1 code implementation30 Aug 2020 Somnath Banerjee, Sahar Ghannay, Sophie Rosset, Anne Vilnat, Paolo Rosso

This paper describes the participation of LIMSI UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text.

Sentiment Analysis

A Metric Learning Approach to Misogyny Categorization

no code implementations WS 2020 Juan Manuel Coria, Sahar Ghannay, Sophie Rosset, Herv{\'e} Bredin

The task of automatic misogyny identification and categorization has not received as much attention as other natural language tasks have, even though it is crucial for identifying hate speech in social Internet interactions.

Metric Learning Sentence Classification +1

A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification

1 code implementation31 Mar 2020 Juan M. Coria, Hervé Bredin, Sahar Ghannay, Sophie Rosset

Despite the growing popularity of metric learning approaches, very little work has attempted to perform a fair comparison of these techniques for speaker verification.

Metric Learning Speaker Verification

End-to-end named entity extraction from speech

no code implementations30 May 2018 Sahar Ghannay, Antoine Caubrière, Yannick Estève, Antoine Laurent, Emmanuel Morin

Until now, NER from speech is made through a pipeline process that consists in processing first an automatic speech recognition (ASR) on the audio and then processing a NER on the ASR outputs.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation

2 code implementations12 May 2018 François Hernandez, Vincent Nguyen, Sahar Ghannay, Natalia Tomashenko, Yannick Estève

We present the recent development on Automatic Speech Recognition (ASR) systems in comparison with the two previous releases of the TED-LIUM Corpus from 2012 and 2014.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

ASR error management for improving spoken language understanding

no code implementations26 May 2017 Edwin Simonnet, Sahar Ghannay, Nathalie Camelin, Yannick Estève, Renato de Mori

This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

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