Journal ArticleDOI
Multidirectional regression (MDR)-based features for automatic voice disorder detection.
Ghulam Muhammad,Tamer A. Mesallam,Khalid H. Malki,Mohamed Farahat,Awais Mahmood,Mansour Alsulaiman +5 more
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TLDR
The results of this study revealed that incorporating voice onset and offset information leads to efficient automatic voice disordered detection.About:
This article is published in Journal of Voice.The article was published on 2012-11-01. It has received 62 citations till now. The article focuses on the topics: Voice analysis & Voice activity detection.read more
Citations
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Journal ArticleDOI
Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.
Shih-Hau Fang,Yu Tsao,Min Jing Hsiao,Ji Ying Chen,Ying-Hui Lai,Feng Chuan Lin,Chi Te Wang,Chi Te Wang,Chi Te Wang +8 more
TL;DR: By stacking several layers of neurons with optimized weights, the proposed DNN algorithm can fully utilize the acoustic features and efficiently differentiate between normal and pathological voice samples.
Journal ArticleDOI
Voice Pathology Detection and Classification Using Auto-Correlation and Entropy Features in Different Frequency Regions
Ahmed Al-nasheri,Ghulam Muhammad,Mansour Alsulaiman,Zulfiqar Ali,Khalid H. Malki,Tamer A. Mesallam,Mohamed F. Ibrahim +6 more
TL;DR: This paper concentrates on developing an accurate and robust feature extraction for detecting and classifying voice pathologies by investigating different frequency bands using autocorrelation and entropy using a support vector machine as a classifier.
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 Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology Detection and Classification.
Ahmed Al-nasheri,Ghulam Muhammad,Mansour Alsulaiman,Zulfiqar Ali,Tamer A. Mesallam,Mohamed Farahat,Khalid H. Malki,Mohamed A. Bencherif +7 more
TL;DR: Investigation of Multidimensional Voice Program parameters to automatically detect and classify the voice pathologies in multiple databases finds a clear difference in the performance of the MDVP parameters using these databases.
Journal ArticleDOI
Patient State Recognition System for Healthcare Using Speech and Facial Expressions
TL;DR: A patient state recognition system for the healthcare framework is proposed in such a way that it provides good recognition accuracy, provides low-cost modeling, and is scalable.
References
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Book
An Introduction to the Psychology of Hearing
TL;DR: In this paper, the nature of sound and the structure and function of the auditory system are discussed, including absolute thresholds, frequency selectivity, masking and the critical band, and the perception of loudness.
Journal ArticleDOI
Dimensionality Reduction of a Pathological Voice Quality Assessment System Based on Gaussian Mixture Models and Short-Term Cepstral Parameters
TL;DR: Using the F-Ratio and Fisher's discriminant ratio, it will be demonstrated that the detection of voice impairments can be performed using both mel cepstral vectors and their first derivative, ignoring the second derivative.
Journal ArticleDOI
Cepstral peak prominence: a more reliable measure of dysphonia.
Yolanda D. Heman-Ackah,Reinhardt J. Heuer,Deirdre D. Michael,Rosemary Ostrowski,Michelle Horman,Margaret M. Baroody,James Hillenbrand,Robert T. Sataloff +7 more
TL;DR: The CPP for running speech is a good predictor and a more reliable measure of dysphonia than are acoustic measures of jitter, shimmer, and NHR.
Journal ArticleDOI
Acoustic Correlates of Vocal Quality
TL;DR: The two most useful parameters for predicting vocal quality were the Pitch Amplitude and the Harmonics-to-Noise Ratio, and no acoustic measure could rank the normal voices.
Journal ArticleDOI
Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors
TL;DR: The Learning Vector quantization methodology demonstrated to be more reliable than the multilayer perceptron architecture yielding 96% frame accuracy under similar working conditions.
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