Multidirectional regression (MDR)-based features for automatic voice disorder detection.
J Voice
; 26(6): 817.e19-27, 2012 Nov.
Article
en En
| MEDLINE
| ID: mdl-23177748
ABSTRACT
BACKGROUND AND OBJECTIVE:
Objective assessment of voice pathology has a growing interest nowadays. Automatic speech/speaker recognition (ASR) systems are commonly deployed in voice pathology detection. The aim of this work was to develop a novel feature extraction method for ASR that incorporates distributions of voiced and unvoiced parts, and voice onset and offset characteristics in a time-frequency domain to detect voice pathology. MATERIALS ANDMETHODS:
The speech samples of 70 dysphonic patients with six different types of voice disorders and 50 normal subjects were analyzed. The Arabic spoken digits (1-10) were taken as an input. The proposed feature extraction method was embedded into the ASR system with Gaussian mixture model (GMM) classifier to detect voice disorder.RESULTS:
Accuracy of 97.48% was obtained in text independent (all digits' training) case, and over 99% accuracy was obtained in text dependent (separate digit's training) case. The proposed method outperformed the conventional Mel frequency cepstral coefficient (MFCC) features.CONCLUSION:
The results of this study revealed that incorporating voice onset and offset information leads to efficient automatic voice disordered detection.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Acústica del Lenguaje
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Medición de la Producción del Habla
/
Calidad de la Voz
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Acústica
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Procesamiento de Señales Asistido por Computador
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Trastornos de la Voz
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Modelos Estadísticos
Tipo de estudio:
Diagnostic_studies
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Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Adolescent
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Adult
/
Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
J Voice
Asunto de la revista:
OTORRINOLARINGOLOGIA
Año:
2012
Tipo del documento:
Article
País de afiliación:
Arabia Saudita