Investigating self-supervised learning for speech enhancement and separation

Autor:	Huang, Zili, Watanabe, Shinji, Yang, Shu-wen, Garcia, Paola, Khudanpur, Sanjeev
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	Speech enhancement and separation are two fundamental tasks for robust speech processing. Speech enhancement suppresses background noise while speech separation extracts target speech from interfering speakers. Despite a great number of supervised learning-based enhancement and separation methods having been proposed and achieving good performance, studies on applying self-supervised learning (SSL) to enhancement and separation are limited. In this paper, we evaluate 13 SSL upstream methods on speech enhancement and separation downstream tasks. Our experimental results on Voicebank-DEMAND and Libri2Mix show that some SSL representations consistently outperform baseline features including the short-time Fourier transform (STFT) magnitude and log Mel filterbank (FBANK). Furthermore, we analyze the factors that make existing SSL frameworks difficult to apply to speech enhancement and separation and discuss the representation properties desired for both tasks. Our study is included as the official speech enhancement and separation downstreams for SUPERB. Comment: To appear in ICASSP 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.07960 Zobrazit plný text záznamu View this record from Arxiv