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GBNF-VAE: A Pathological Voice Enhancement Model Based on Gold Section for Bottleneck Feature With Variational

Ganjun Liu1, Tao Zhang1, Biyun Ding1

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China.

Journal of Voice : Official Journal of the Voice Foundation
|May 11, 2023
PubMed
Summary

This study introduces a new speech enhancement model, GBNF-VAE, to improve pathological speech quality by separating timbre and semantic features. The model effectively reduces airflow noise, leading to clearer, enhanced speech.

Keywords:
Bottleneck featureGolden sectionPathological speech enhancementVariational autoencoder

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Area of Science:

  • Speech processing
  • Biomedical engineering
  • Signal processing

Background:

  • Speech enhancement aims to improve degraded speech signals.
  • Existing methods often overlook pathological speech quality issues caused by anomalous glottis flow.
  • Effective enhancement requires separating high-dimensional timbre and speech features to suppress low-dimensional noise.

Purpose of the Study:

  • To propose an effective enhancement model for pathological speech.
  • To address the challenge of anomalous glottis flow affecting speech quality.
  • To extract and combine high-dimensional timbre and semantic features for improved speech synthesis.

Main Methods:

  • Proposed the GBNF-VAE model for efficient timbre extraction and reduction of airflow noise interference.
  • Utilized the Golden Section method to control bottleneck features for efficient timbre characterization.
  • Employed a variational autoencoder to extract semantic features, combined with timbre features for enhanced speech synthesis.

Main Results:

  • The GBNF-VAE model demonstrated outstanding performance in pathological speech quality enhancement.
  • Evaluations included spectrum observation, objective indicators, and subjective assessments.
  • The proposed method effectively suppressed anomalous airflow noise and improved speech quality.

Conclusions:

  • The GBNF-VAE model offers a significant advancement in pathological speech enhancement.
  • Separating timbre and semantic features is crucial for improving speech quality in pathological cases.
  • The model's efficiency and effectiveness are validated by comprehensive performance evaluations.