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Short-time speaker verification with different speaking style utterances.

Hongwei Mao1, Yan Shi1, Yue Liu1

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Summary
This summary is machine-generated.

This study introduces a new corpus for automatic speaker verification (ASV) with diverse speaking styles like singing and humming. Combining these styles significantly improves ASV system performance, especially with advanced x-vector models.

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Area of Science:

  • Speech Processing
  • Biometrics
  • Machine Learning

Background:

  • Automatic Speaker Verification (ASV) has advanced technically but struggles with spoofing and varied conditions.
  • Existing ASV research primarily uses natural speech, limiting robustness.
  • Sensitivity to unknown conditions hinders ASV technology adoption.

Purpose of the Study:

  • To design and release a novel ASV corpus featuring multi-speaking styles (singing, humming, normal reading).
  • To investigate the robustness of ASV systems across different speaking styles.
  • To analyze speaker discrimination and variability within and across speaking styles.

Main Methods:

  • Developed a new ASV corpus with text-dependent and independent singing, humming, and reading utterances, released on Zenodo.
  • Investigated speaker discrimination in feature space for each speaking style.
  • Employed Gaussian Mixture Models (GMM) and x-vector embeddings for ASV system development and evaluation.

Main Results:

  • Humming and singing speech contain more distinguishable voiceprint information than normal reading speech for traditional ASV systems.
  • Combining singing, humming, and reading styles significantly enhances x-vector based ASV systems.
  • Limited performance gains were observed when combining styles for conventional GMM-based systems.

Conclusions:

  • The proposed multi-style ASV corpus facilitates research into system robustness.
  • Incorporating diverse speaking styles, particularly singing and humming, improves ASV performance.
  • Advanced methods like x-vectors benefit more from multi-style data fusion compared to GMMs.