Attention-based latent features for jointly trained end-to-end automatic speech recognition with modified speech enhancement

In this paper, we propose a joint training framework that efficiently combines time-domain speech enhancement (SE) with an end-to-end (E2E) automatic speech recognition (ASR) system utilizing attention-based latent features. Using the latent feature to train E2E ASR implies that various time-domain...

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Detalles Bibliográficos

Principais autores:	Da-Hee Yang, Joon-Hyuk Chang
Formato:	Artigo
Idioma:	Inglês
Publicado:	Springer 2023-03-01
Series:	Journal of King Saud University: Computer and Information Sciences
Assuntos:	Time-domain speech enhancement End-to-end automatic speech recognition Attention-based latent feature Joint training framework
Acceso en liña:	http://www.sciencedirect.com/science/article/pii/S1319157823000368
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