Time-Domain Speech Enhancement for Robust Automatic Speech Recognition
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an attentive recurrent network (ARN) to bridge speech enhancement and automatic speech recognition (ASR). The ARN model significantly improves ASR performance in noisy environments by enhancing speech intelligibility.
Area Of Science
- Speech Processing
- Machine Learning
- Artificial Intelligence
Background
- Speech enhancement algorithms improve noisy speech intelligibility.
- Speech enhancement is not yet a proven effective frontend for robust automatic speech recognition (ASR) in noisy conditions.
- A gap exists between speech enhancement and ASR, hindering robust ASR system progress.
Purpose Of The Study
- To eliminate the divide between speech enhancement and ASR.
- To propose a novel attentive recurrent network (ARN) based time-domain enhancement model.
- To enable a fully decoupled speech enhancement and acoustic model trained solely on clean speech.
Main Methods
- Developed an attentive recurrent network (ARN) for time-domain speech enhancement.
- Designed a system that fully decouples speech enhancement from the acoustic model.
- Trained the acoustic model exclusively on clean speech data.
Main Results
- The ARN enhanced speech significantly improved ASR results on the CHiME-2 corpus.
- Achieved an average word error rate of 6.28%.
- Outperformed previous best results by a relative margin of 19.3%.
Conclusions
- The proposed ARN-based speech enhancement effectively bridges the gap between enhancement and ASR.
- The decoupled system demonstrates superior performance for robust ASR in noisy conditions.
- This approach advances the development of more effective robust ASR systems.
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