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Nauman Dawalatabad

Alumni

Publications

ECAPA-TDNN Embeddings for Speaker Diarization
Nauman Dawalatabad
Mirco Ravanelli
François Grondin
Jenthe Thienpondt
Brecht Desplanques
Hwidong Na
Learning robust speaker embeddings is a crucial step in speaker diarization. Deep neural networks can accurately capture speaker discriminat… (see more)ive characteristics and popular deep embeddings such as x-vectors are nowadays a fundamental component of modern diarization systems. Recently, some improvements over the standard TDNN architecture used for x-vectors have been proposed. The ECAPA-TDNN model, for instance, has shown impressive performance in the speaker verification domain, thanks to a carefully designed neural model. In this work, we extend, for the first time, the use of the ECAPA-TDNN model to speaker diarization. Moreover, we improved its robustness with a powerful augmentation scheme that concatenates several contaminated versions of the same signal within the same training batch. The ECAPA-TDNN model turned out to provide robust speaker embeddings under both close-talking and distant-talking conditions. Our results on the popular AMI meeting corpus show that our system significantly outperforms recently proposed approaches.
2021-12-31
Conference of the International Speech Communication Association (unknown)
doi.org
arxiv.org
SpeechBrain: A General-Purpose Speech Toolkit
Mirco Ravanelli
Titouan Parcollet
Peter Plantinga
Aku Rouhe
Samuele Cornell
Loren Lugosch
Cem Subakan
Nauman Dawalatabad
Abdelwahab Heba
Jianyuan Zhong
Ju-chieh Chou
Sung-Lin Yeh
Szu-wei Fu
Chien-Feng Liao
Elena Rastorgueva
François Grondin
William Aris
Hwidong Na
Yan Gao
Renato De Mori … (see 1 more)
Yoshua Bengio
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the research and development of neural speech proc… (see more)essing technologies by being simple, flexible, user-friendly, and well-documented. This paper describes the core architecture designed to support several tasks of common interest, allowing users to naturally conceive, compare and share novel speech processing pipelines. SpeechBrain achieves competitive or state-of-the-art performance in a wide range of speech benchmarks. It also provides training recipes, pretrained models, and inference scripts for popular speech datasets, as well as tutorials which allow anyone with basic Python proficiency to familiarize themselves with speech technologies.
2020-12-31
arXiv (preprint)
doi.org
arxiv.org