Papers

Feature Normalization for Fine-tuning Self-Supervised Models in Speech Enhancement

International Conference
2021~
작성자
dsp
작성일
2023-08-11 11:23
조회
632
Authors : Hejung Yang, Hong-Goo Kang

Year : 2023

Publisher / Conference : INTERSPEECH

Research area : Speech Signal Processing, Speech Enhancement, Etc

Presentation : Poster

Large, pre-trained representation models trained using self-supervised learning have gained popularity in various fields of machine learning because they are able to extract high-quality salient features from input data. As such, they have been frequently used as base networks for various pattern classification tasks such as speech recognition. However, not much research has been conducted on applying these types of models to the field of speech signal generation. In this paper, we investigate the feasibility of using pre-trained speech representation models for a downstream speech enhancement task. To alleviate mismatches between the input features of the pre-trained model and the target enhancement model, we adopt a novel feature normalization technique to smoothly link these modules together. Our proposed method enables significant improvements in speech quality compared to baselines when combined with various types of pre-trained speech models.
전체 360
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30 International Conference Yanjue Song, Doyeon Kim, Hong-Goo Kang, Nilesh Madhu "Spectrum-aware neural vocoder based on self-supervised learning for speech enhancement" in EUSIPCO, 2024
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25 International Conference Hejung Yang, Hong-Goo Kang "On Fine-Tuning Pre-Trained Speech Models With EMA-Target Self-Supervised Loss" in ICASSP, 2024
24 International Conference Hong-Goo Kang, W. Bastiaan Kleijn, Jan Skoglund, Michael Chinen "Convolutional Transformer for Neural Speech Coding" in Audio Engineering Society Convention, 2023
23 International Conference Hong-Goo Kang, Jan Skoglund, W. Bastiaan Kleijn, Andrew Storus, Hengchin Yeh "A High-Rate Extension to Soundstream" in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2023