Voice Pathology Detection and Classification Using Auto-Correlation and Entropy Features in Different Frequency Regions

Al-nasheri, Ahmed; Muhammad, Ghulam; Alsulaiman, Mansour; Ali, Zulfiqar; Malki, Khalid H.; Mesallam, Tamer A.; Farahat, Mohamed

doi:10.1109/access.2017.2696056

Cited by 123 publications

(45 citation statements)

References 28 publications

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“…There are several existing solutions in the field of pathological speech detection [9,10]. For example, Al-Nasheri et al in their work [11] concentrated on developing feature extraction for the detection and classification of voice pathologies by investigating different frequency bands using autocorrelation and entropy. The voice impairment cases studied were caused by vocal cysts, vocal polyps, and vocal paralysis.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Approach to Dysphonia Detection

et al. 2018

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This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech features and used these to train the classification model. As classifiers, three state-of-the-art methods were used: K-nearest neighbors, random forests, and support vector machine. We analyzed the performance of classifiers with and without gender taken into account. The experimental results showed that it is possible to recognize pathological speech with as high as a 91.3% classification accuracy.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Approach to Dysphonia Detection

et al. 2018

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“…[36,17,26], etc. From the voice pathologies point of view, most researchers restricted the dataset to a limited set of pathologies [7,43,14,25,51,44,5,3,4,2].…”

Section: Introductionmentioning

confidence: 99%

Towards robust voice pathology detection

Harár

Galáž

Alonso-Hernández

et al. 2018

Neural Comput & Applic

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Automatic objective non-invasive detection of pathological voice based on computerized analysis of acoustic signals can play an important role in early diagnosis, progression tracking and even effective treatment of pathological voices. In search towards such a robust voice pathology detection system we investigated 3 distinct classifiers within supervised learning and anomaly detection paradigms. We conducted a set of experiments using a variety of input data such as raw waveforms, spectrograms, mel-frequency cepstral coefficients (MFCC) and conventional acoustic (dysphonic) features (AF). In comparison with previously published works, this article is the first to utilize combination of 4 different databases comprising normophonic and pathological recordings of sustained phonation of the vowel /a/ unrestricted to a subset of vocal pathologies. Furthermore, to our best knowledge, this article is the first to explore gradient boosted trees and deep learning for this application. The following best classification performances measured by F1 score on dedicated test set were achieved: XGBoost (0.733) using AF and MFCC, DenseNet (0.621) using MFCC, and Isolation Forest (0.610) using AF. Even though these results are of exploratory character, conducted experiments do show promising potential of gradient boosting and deep learning methods to robustly detect voice pathologies.

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“…In addition, al-Nasheri et al [11] used the SVD and MEEI databases and used the autocorrelation and entropy parameters for the detection and classification of pathologies, obtaining respectively 99.96% and 92.79% accuracy for MEEI and SVD.…”

Section: Discussion and Validationmentioning

confidence: 99%

“…The expression where true negative (TN) can be explained as follows: the system detects a normal subject as a normal subject, while the true positive (TP) means that the system detects a pathological subject as a pathological subject, besides the false negative (FN) means that the system detects a pathological issue as a normal subject and ultimately false positives (FP) means that the system detects a normal subject matter as a pathological subject. [11] The extracted parameters from the two different databases must be checked in the detection and classification processes.…”

Section: The Noise To Harmonics Ratio Combined With Detrendedmentioning

confidence: 99%

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Voice Pathology Recognition and Classification using Noise Related Features

Hamdi¹,

Salah²,

Cherif³

2018

ijacsa

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Nowadays, the diseases of the voice increase because of bad social habits and the misuse of voice. These pathologies should be treated from the beginning. Indeed, it is no longer necessary that the diseases of the voice lead to affect the quality of the voice as heard by a listener. The most useful tool for diagnosing such diseases is the Acoustic analysis. We present in this work, new expression parameters in order to clarify the description of the vocal signal. These parameters help to classify the unhealthy voices. They describe essentially the fundamental frequency F0, the Harmonics-to-Noise report (HNR), the report Noise to Harmonics Ratio (NHR) and Detrended Fluctuation Analysis (DFA). The classification is performed on two Saarbruecken Voice and MEEI pathological databases using HTK classifiers. We can classify them into two different types: the first classification is binary which is used for the normal and pathological voices; the second one is called a four-category classification used in spasmodic, polyp, nodule and normal female voices and male speakers. And we studied the effects of these new parameters when combined with the MFCC, Delta, Delta second and Energy coefficients.

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Voice Pathology Detection and Classification Using Auto-Correlation and Entropy Features in Different Frequency Regions

Cited by 123 publications

References 28 publications

Machine Learning Approach to Dysphonia Detection

Machine Learning Approach to Dysphonia Detection

Towards robust voice pathology detection

Voice Pathology Recognition and Classification using Noise Related Features

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