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Structured Sparse Spectral Transforms and Structural Measures for Voice Conversion.

Yunxin Zhao1, Mili Kuruvilla-Dugdale2, Minguang Song1

  • 1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211 USA.

IEEE/ACM Transactions on Audio, Speech, and Language Processing
|January 28, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a structured sparse spectral transform for voice conversion (VC), improving speech naturalness and similarity. The method effectively reduces muffled speech by optimizing transform matrices for better spectral shaping and frequency warping.

Keywords:
NMFVoice conversionfrequency warpingobjective measuresstructured sparse spectral transform

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Area of Science:

  • Speech Processing
  • Machine Learning
  • Digital Signal Processing

Background:

  • High-dimensional data in voice conversion (VC) often leads to overfit transform matrices and muffled speech.
  • Existing methods struggle with simultaneous frequency warping and spectral shaping in VC.

Purpose of the Study:

  • To develop a structured sparse spectral transform method for voice conversion.
  • To address muffled speech by optimizing transform matrices for high-dimensional STRAIGHT spectra.
  • To improve speech naturalness and target speaker similarity in converted speech.

Main Methods:

  • Investigated a structured sparse spectral transform for frequency warping and spectral shaping.
  • Utilized the frequency-warping characteristic to define a region of support (ROS) in the transform matrix.
  • Applied nonnegative matrix factorization (NMF) to obtain a structured sparse transform.
  • Developed structural measures of spectral and temporal covariance/variance for quality assessment.

Main Results:

  • Embedding ROS in spectral transforms allows flexible trade-offs between spectral distortion and structure preservation.
  • Structural measures provided quantitatively reasonable results for converted speech quality.
  • Subjective listening tests showed the proposed VC method achieved a "very good" mean opinion score, outperforming other methods in naturalness and voice similarity.

Conclusions:

  • The proposed structured sparse spectral transform method significantly enhances voice conversion quality.
  • The approach effectively mitigates muffled speech and improves naturalness and speaker similarity.
  • Structural measures offer a reliable quantitative assessment of converted speech quality.