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Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization.

Chunying Fang1, Haifeng Li2, Lin Ma2

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China; School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, China.

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Summary
This summary is machine-generated.

This study introduces a novel method for evaluating pathological speech intelligibility using S-transform and chaotic analysis. The optimized feature set significantly improves recognition rates for distorted speech.

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Area of Science:

  • Speech Pathology
  • Signal Processing
  • Machine Learning

Background:

  • Pathological speech, characterized by distortions from illness, poses challenges for automatic intelligibility assessment due to its non-stationary and mutational nature.
  • Accurate evaluation of pathological speech is crucial for expert assistance and diagnosis.

Purpose of the Study:

  • To develop an innovative feature extraction and reduction method for pathological speech intelligibility evaluation.
  • To create a robust and reliable system for automatic assessment of speech distortions.

Main Methods:

  • A novel feature set generation using S-transform and chaotic analysis, incorporating basic acoustics features (BAFS), Mel S-transform cepstrum coefficients (MSCC), and chaotic features.
  • Hierarchical visual fusion using radar charts and F-score for optimizing the feature set from 526 to 96-104 dimensions.
  • Support Vector Machine (SVM) classification for performance evaluation on NKI-CCRT and SVD corpora.

Main Results:

  • The optimized feature set achieved a recognition rate of 84.4% on the NKI-CCRT corpus and 78.7% on the SVD corpus.
  • The proposed method demonstrated significant dimensionality reduction while maintaining high performance.

Conclusions:

  • The novel feature extraction and optimization method is effective and reliable for pathological speech intelligibility evaluation.
  • The integration of S-transform, chaotic analysis, and hierarchical visual fusion offers a promising approach for analyzing complex speech patterns.