title: Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization.
abstract: 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 onS-transform and chaotic analysis is proposed. There are BAFS (430, basic acoustics feature), local spectral characteristics MSCC (84, MelS-transform cepstrum coefficients), and chaotic features (12). Finally, radar chart andF-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.
authors.affiliation: School 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.
authors.forename: Chunying
authors.identifier: 0000-0003-0860-1014
authors.initials: C
authors.lastname: Fang
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Haifeng
authors.identifier: 0000-0002-2534-2299
authors.initials: H
authors.lastname: Li
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Ma
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Mancai
authors.initials: M
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D005260:Female; D005583:Fourier Analysis; D006258:Head and Neck Neoplasms / Q000150:complications* / Q000503:physiopathology; D006801:Humans; D008297:Male; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D013060:Speech; D013061:Speech Acoustics; D013064:Speech Disorders / Q000209:etiology* / Q000503:physiopathology / Q000534:rehabilitation*; D013066:Speech-Language Pathology / Q000379:methods*; D060388:Support Vector Machine