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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5282458/ https://www.ncbi.nlm.nih.gov/pubmed/28194222 http://dx.doi.org/10.1155/2017/2431573 |
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author | Fang, Chunying Li, Haifeng Ma, Lin Zhang, Mancai |
author_facet | Fang, Chunying Li, Haifeng Ma, Lin Zhang, Mancai |
author_sort | Fang, Chunying |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5282458 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-52824582017-02-13 Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization Fang, Chunying Li, Haifeng Ma, Lin Zhang, Mancai Comput Math Methods Med Research Article 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. Hindawi Publishing Corporation 2017 2017-01-17 /pmc/articles/PMC5282458/ /pubmed/28194222 http://dx.doi.org/10.1155/2017/2431573 Text en Copyright © 2017 Chunying Fang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Fang, Chunying Li, Haifeng Ma, Lin Zhang, Mancai Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization |
title | Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization |
title_full | Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization |
title_fullStr | Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization |
title_full_unstemmed | Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization |
title_short | Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization |
title_sort | intelligibility evaluation of pathological speech through multigranularity feature extraction and optimization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5282458/ https://www.ncbi.nlm.nih.gov/pubmed/28194222 http://dx.doi.org/10.1155/2017/2431573 |
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