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Your search keyword '"Dowerah, Sandipana"' showing total 3 results
Start Over Author "Dowerah, Sandipana" Topic [info.info-hc]computer science [cs]/human-computer interaction [cs.hc]
3 results on '"Dowerah, Sandipana"'

1. Self-supervised learning with diffusion-based multichannel speech enhancement for speaker verification under noisy conditions

Author

Dowerah, Sandipana, Kulkarni, Ajinkya, Serizel, Romain, Jouvet, Denis, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, multichannel speech enhancement, Sound (cs.SD), diffusion probabilistic models, Audio and Speech Processing (eess.AS), self-supervised learning, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], multichannel speech enhancement diffusion probabilistic models speaker verification self-supervised learning, speaker verification, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

International audience; The paper introduces Diff-Filter, a multichannel speech enhancement approach based on the diffusion probabilistic model, for improving speaker verification performance under noisy and reverberant conditions. It also presents a new two-step training procedure that takes the benefit of self-supervised learning. In the first stage, the Diff-Filter is trained by conducting timedomain speech filtering using a scoring-based diffusion model. In the second stage, the Diff-Filter is jointly optimized with a pre-trained ECAPA-TDNN speaker verification model under a self-supervised learning framework. We present a novel loss based on equal error rate. This loss is used to conduct selfsupervised learning on a dataset that is not labelled in terms of speakers. The proposed approach is evaluated on MultiSV, a multichannel speaker verification dataset, and shows significant improvements in performance under noisy multichannel conditions.

Published
2023

2. Joint Optimization of Diffusion Probabilistic-Based Multichannel Speech Enhancement with Far-Field Speaker Verification

Author

Dowerah, Sandipana, Serizel, Romain, Jouvet, Denis, Mohammadamini, M, Matrouf, Driss, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, and ANR-18-CE33-0014,ROBOVOX,ROBOVOX - Identification vocale robuste pour les robots de sécurité mobiles(2018)

Subjects
multichannel speech enhancement, far-field speaker verification, diffusion model, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]
Abstract

International audience; Today's smart devices using speaker verification are getting equipped with multiple microphones resulting in improving spatial ambiguity and directivity. However, unlike any other speech-based applications, the performance of speaker verification degrades in far-field scenarios due to the adverse effects of a noisy environment and room reverberation. This paper presents a novel multichannel speech enhancement module based on the diffusion probabilistic model. It is used as the front-end of the ECAPA-TDNN speaker verification system in far-field scenarios under a noisy-reverberant environment. The proposed system incorporates a two-stage training approach. In the first stage, both speech enhancement and speaker verification modules are trained individually. In the second stage, both the modules are combined to jointly trained them. We use similaritypreserving knowledge distillation loss that guides the network to produce similar activation for enhanced signals to that of clean speech signals. Using joint optimization with knowledge distillation loss achieved the best performance on both the evaluation composed of synthetic clips similar to those used at training and on unseen recorded clips from the VOiCES dataset.

Published
2023
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3. How to Leverage DNN-based speech enhancement for multi-channel speaker verification?

Author

Dowerah, Sandipana, Serizel, Romain, Jouvet, Denis, Mohammadamini, Mohammad, Matrouf, Driss, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, and ANR-18-CE33-0014,ROBOVOX,ROBOVOX - Identification vocale robuste pour les robots de sécurité mobiles(2018)

Subjects
FOS: Computer and information sciences, multichannel speech enhancement, Sound (cs.SD), far-field speaker verification, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, deep neural network, Computer Science - Human-Computer Interaction, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Computer Science - Sound, Human-Computer Interaction (cs.HC), Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

International audience; Speaker verification (SV) suffers from unsatisfactory performance in far-field scenarios due to environmental noise andthe adverse impact of room reverberation. This work presents a benchmark of multichannel speech enhancement for far-fieldspeaker verification. One approach is a deep neural network-based, and the other is a combination of deep neural network andsignal processing. We integrated a DNN architecture with signal processing techniques to carry out various experiments. Ourapproach is compared to the existing state-of-the-art approaches. We examine the importance of enrollment in pre-processing,which has been largely overlooked in previous studies. Experimental evaluation shows that pre-processing can improve the SVperformance as long as the enrollment files are processed similarly to the test data and that test and enrollment occur within similarSNR ranges. Considerable improvement is obtained on the generated and all the noise conditions of the VOiCES dataset.

Published
2022

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