Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization

Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary an...

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Bibliographic Details
Published in:Computational and mathematical methods in medicine 2017-01, Vol.2017 (2017), p.1-8
Main Authors: Zhang, Mancai, Ma, Lin, Li, Haifeng, Fang, Chunying
Format: Article
Language:English
Subjects:
Algorithms
Computer Graphics
Female
Fourier Analysis
Head and Neck Neoplasms - complications
Head and Neck Neoplasms - physiopathology
Humans
Male
Models, Statistical
Nonlinear Dynamics
Pattern Recognition, Automated
Speech
Speech Acoustics
Speech Disorders - etiology
Speech Disorders - physiopathology
Speech Disorders - rehabilitation
Speech-Language Pathology - methods
Support Vector Machine
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Summary:Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary and mutational. In this paper, we carry out an independent innovation of feature extraction and reduction, and we describe a multigranularity combined feature scheme which is optimized by the hierarchical visual method. A novel method of generating feature set based on S-transform and chaotic analysis is proposed. There are BAFS (430, basic acoustics feature), local spectral characteristics MSCC (84, Mel S-transform cepstrum coefficients), and chaotic features (12). Finally, radar chart and F-score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96 dimensions based on NKI-CCRT corpus and 104 dimensions based on SVD corpus. The experimental results denote that new features by support vector machine (SVM) have the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus and 78.7% on SVD corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation.
ISSN:1748-670X
1748-6718
DOI:10.1155/2017/2431573