Year: 2024
Type: conference-paper
Abstract: We explore a speech enhancement framework where neural speech embeddings, derived from pre-trained self-supervised learning (SSL) models applied to noisy signals, are used as inputs to a neural vocoder to generate the corresponding clean speech. The primary innovation lies in enhancing these latent neural embeddings to mitigate distortions caused by noise and reverberation, resulting in a superior quality of the synthesized signal. By dividing the process into Separate phases for embedding enhancement and speech generation, the approach allows for greater flexibility in network design. We also examine the advantage of integrating hidden states from the SSL model in a learnable manner to create a more robust embedding for the vocoder input. Additionally, we investigate various loss functions for training the neural vocoder. Experimental results confirm the effectiveness of our proposed approach, particularly in environments with simultaneous background noise and reverberation.
Institutions: Yonsei University, Ghent University, IMEC
Language: English
Topic: Speech and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Yonsei University
OpenAlex ID: W4406858747