Separation (statistics)

Source separation

Computer science

Short-time Fourier transform

Speech enhancement

Pattern recognition (psychology)

Mathematics

Fourier analysis

Speech processing

Performance enhancement

Mathematical analysis

Artificial intelligence

Medicine

Physical medicine and rehabilitation

Noise reduction

Voice activity detection

Fourier transform

Machine learning

Speech recognition

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.

Investigating Self-Supervised Learning for Speech Enhancement and Separation