Proceedings ArticleDOI
Vocal fold disorder detection based on continuous speech by using MFCC and GMM
Zulfiqar Ali,Mansour Alsulaiman,Ghulam Muhammad,Irraivan Elamvazuthi,Tamer A. Mesallam +4 more
- pp 292-297
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TLDR
Mel-frequency cepstral coefficients (MFCC) are used with Gaussian mixture model (GMM) to build an automatic detection system capable of differentiating normal and pathological voices.Abstract:
Vocal fold voice disorder detection with a sustained vowel is well investigated by research community during recent years. The detection of voice disorder with a sustained vowel is a comparatively easier task than detection with continuous speech. The speech signal remains stationary in case of sustained vowel but it varies over time in continuous time. This is the reason; voice detection by using continuous speech is challenging and demands more investigation. Moreover, detection with continuous speech is more realistic because people use it in their daily conversation but sustained vowel is not used in everyday talks. An accurate voice assessment can provide unique and complementary information for the diagnosis, and can be used in the treatment plan. In this paper, vocal fold disorders, cyst, polyp, nodules, paralysis, and sulcus, are detected using continuous speech. Mel-frequency cepstral coefficients (MFCC) are used with Gaussian mixture model (GMM) to build an automatic detection system capable of differentiating normal and pathological voices. The detection rate of the developed detection system with continuous speech is 91.66%.read more
Citations
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Journal ArticleDOI
Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms
Tamer A. Mesallam,Mohamed Farahat,Khalid H. Malki,Mansour Alsulaiman,Zulfiqar Ali,Ahmed Al-nasheri,Ghulam Muhammad +6 more
TL;DR: An Arabic voice pathology database (AVPD) is designed and developed in this study by recording three vowels, running speech, and isolated words and the shortcomings of different voice disorder databases were identified so that they could be avoided in the AVPD.
Journal ArticleDOI
A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders.
TL;DR: A survey of research work conducted on automatic detection of voice disorders and how it is able to identify the different types of voice Disorders is presented.
Journal ArticleDOI
An intelligent healthcare system for detection and classification to discriminate vocal fold disorders
TL;DR: The results show that the proposed intelligent healthcare system is accurate and reliable in vocal fold disorder assessment and can be deployed successfully for remote diagnosis and is better as compared to existing disorder assessment systems.
Journal ArticleDOI
An Automatic Health Monitoring System for Patients Suffering From Voice Complications in Smart Cities
TL;DR: An automatic voice disorder detection system to monitor the resident of all age group and professional backgrounds is implemented and it is found that lower frequencies from 1 to 1562 Hz contributes significantly in the detection of voice disorders.
Journal ArticleDOI
Automatic Voice Pathology Detection With Running Speech by Using Estimation of Auditory Spectrum and Cepstral Coefficients Based on the All-Pole Model.
TL;DR: The developed system can effectively be used in voice pathology detection and classification systems, and the proposed features can visually differentiate between normal and pathological samples.
References
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