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Found 4 papers, 2 papers with code
LC4SV: A Denoising Framework Learning to Compensate for Unseen Speaker Verification Models
The performance of speaker verification (SV) models may drop dramatically in noisy environments.
D4AM: A General Denoising Framework for Downstream Acoustic Models
To our knowledge, this is the first work that deploys an effective combination scheme of regression (denoising) and classification (ASR) objectives to derive a general pre-processor applicable to various unseen ASR systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling
NASTAR uses a feedback mechanism to simulate adaptive training data via a noise extractor and a retrieval model.
SERIL: Noise Adaptive Speech Enhancement using Regularization-based Incremental Learning
The results verify that the SERIL model can effectively adjust itself to new noise environments while overcoming the catastrophic forgetting issue.
