Perceive and Predict: Self-Supervised Speech Representation Based Loss Functions for Speech Enhancement
Author:
Affiliation:
1. University of Sheffield,Department of Computer Science
Funder
UK Research and Innovation
Health
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10095666.pdf?arnumber=10095666
Reference30 articles.
1. SUPERB: Speech Processing Universal PERformance Benchmark
2. XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
3. Att-TasNet: Attending to Encodings in Time-Domain Audio Speech Separation of Noisy, Reverberant Speech Mixtures
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1. Multi-CMGAN+/+: Leveraging Multi-Objective Speech Quality Metric Prediction for Speech Enhancement;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
2. Curricular Contrastive Regularization for Speech Enhancement with Self-Supervised Representations;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
3. Improving Speech Perceptual Quality and Intelligibility Through Sub-band Temporal Envelope Characteristics;Communications in Computer and Information Science;2024
4. The Effect of Spoken Language on Speech Enhancement Using Self-Supervised Speech Representation Loss Functions;2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA);2023-10-22
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