핵심 개념
SSL representations have limited value in improving on-device speech enhancement systems under low-SNR conditions.
초록
Self-supervised learned models are effective for speech tasks like ASR, but their utility in speech enhancement is limited.
Investigating the impact of SSL representations on single-channel speech enhancement.
Proposed techniques include knowledge distillation and pre-training using SSL embeddings.
Experiment results show little improvement in speech enhancement under on-device constraints.
Pre-training with SSL embeddings does not significantly enhance the base model.
Structure analysis of Wav2Vec2 embeddings reveals challenges in utilizing them for enhancement.
Knowledge distillation from SSL models proves challenging due to the intricate details captured in the embeddings.
통계
"Our constraints are designed around on-device real-time speech enhancement – model is causal, the compute footprint is small."
"In particular, we study the popular wav2vec2.0 SSL model and attempt to utilize it to improve a GCRN based on-device SE model."
"The GCRN neural architecture can be used to design and develop an SE models satisfying these characteristics."
인용구
"Our goal in this paper is to systematically investigate different ways of using SSL embeddings to improve an SE system."
"SSL models are usually very large, non-causal and hence fine-tuning them is not a possible path for using them in our case."