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Found 5 papers, 4 papers with code
CasNet: Investigating Channel Robustness for Speech Separation
In this study, inheriting the use of our previously constructed TAT-2mix corpus, we address the channel mismatch problem by proposing a channel-aware audio separation network (CasNet), a deep learning framework for end-to-end time-domain speech separation.
Improving generalizability of distilled self-supervised speech processing models under distorted settings
Self-supervised learned (SSL) speech pre-trained models perform well across various speech processing tasks.
Universal Adaptor: Converting Mel-Spectrograms Between Different Configurations for Speech Synthesis
Most recent speech synthesis systems are composed of a synthesizer and a vocoder.
Disentangling the Impacts of Language and Channel Variability on Speech Separation Networks
However, domain mismatch between training/test situations due to factors, such as speaker, content, channel, and environment, remains a severe problem for speech separation.
Dual-Path Filter Network: Speaker-Aware Modeling for Speech Separation
DPFN is composed of two parts: the speaker module and the separation module.
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