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SASEGAN-TCN: Speech enhancement algorithm based on self-attention generative adversarial network and temporal

Rongchuang Lv1, Niansheng Chen1, Songlin Cheng1

  • 1School of Electronic Information Engineering, Shanghai Dianji University, Shanghai 201306, China.

Mathematical Biosciences and Engineering : MBE
|March 29, 2024
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Summary

This study introduces a novel SASEGAN-TCN model for speech enhancement, improving signal quality by aggregating feature information. The model significantly enhances perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) scores.

Keywords:
autoencoderdeep learninggenerative adversarial networkspeech enhancement

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Area of Science:

  • Artificial Intelligence
  • Signal Processing
  • Machine Learning

Background:

  • Traditional unsupervised speech enhancement models struggle with input feature information aggregation, leading to noise and reduced speech quality.
  • Non-aggregation of speech features during training introduces artifacts, negatively impacting overall performance.

Purpose of the Study:

  • To analyze the impact of non-aggregated input speech features on model performance.
  • To propose a novel speech enhancement model that addresses these limitations and improves training stability.

Main Methods:

  • Introduced a temporal convolutional neural network (TCN) integrated into the SASEGAN architecture.
  • Developed the SASEGAN-TCN model to capture local speech features and aggregate global information.

Main Results:

  • Achieved a Perceptual Evaluation of Speech Quality (PESQ) score of 2.1636 and Short-Time Objective Intelligibility (STOI) of 92.78% on the Valentini dataset.
  • Attained a PESQ score of 1.8077 and STOI of 83.54% on the THCHS30 dataset.
  • Reduced speech recognition error rate by 17.4% when using enhanced speech data with an acoustic model.

Conclusions:

  • The SASEGAN-TCN model effectively enhances speech quality and intelligibility.
  • The proposed model demonstrates superior performance and training stability compared to baseline methods.
  • Speech enhancement using SASEGAN-TCN leads to significant improvements in downstream tasks like speech recognition.