Voice pathology detection using auto-correlation of different filters bank

Al-nasheri, Ahmed; Ali, Zulfiqar; Muhammad, Ghulam; Alsulaiman, Mansour

doi:10.1109/aiccsa.2014.7073178

Cited by 19 publications

(14 citation statements)

References 25 publications

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“…The precision, recall and f1-score of validation samples is shown in Tab. V. [6] we assume that further investigation is in place and could lead to much better results.…”

Section: Resultsmentioning

confidence: 94%

Voice Pathology Detection Using Deep Learning: a Preliminary Study

Harár

Alonso-Hernandezy

Mekyska

et al. 2017

2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI)

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This paper describes a preliminary investigation of Voice Pathology Detection using Deep Neural Networks (DNN). We used voice recordings of sustained vowel /a/ produced at normal pitch from German corpus Saarbruecken Voice Database (SVD). This corpus contains voice recordings and electroglottograph signals of more than 2 000 speakers. The idea behind this experiment is the use of convolutional layers in combination with recurrent Long-Short-Term-Memory (LSTM) layers on raw audio signal. Each recording was split into 64 ms Hamming windowed segments with 30 ms overlap. Our trained model achieved 71.36 % accuracy with 65.04 % sensitivity and 77.67 % specificity on 206 validation files and 68.08 % accuracy with 66.75 % sensitivity and 77.89 % specificity on 874 testing files. This is a promising result in favor of this approach because it is comparable to similar previously published experiment that used different methodology. Further investigation is needed to achieve the state-of-the-art results.

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“…The precision, recall and f1-score of validation samples is shown in Tab. V. [6] we assume that further investigation is in place and could lead to much better results.…”

Section: Resultsmentioning

confidence: 94%

Voice Pathology Detection Using Deep Learning: a Preliminary Study

Harár

Alonso-Hernandezy

Mekyska

et al. 2017

2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI)

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“…More specifically, most researchers have analyzed sustained phonation of the vowel /a/, e.g. [1,43,7,14] due to its presence in most databases (language-independent speech task [59]). Some researchers also analyzed a combination of the vowels, e.g.…”

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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“…We note here the most used databases such as the database (MEEI) [11,3], Saarbruecken Voice Database (SVD) [4,7,12] and Arabic Voice Pathology Database (AVPD) [13,7]. The research carried out on these bases is generally based on the analysis of the phonation of the vowel /a/, for example in the works [14,15,16,17]. While in other works some researchers have combined vowels to do the analysis, for example [5,6,18].…”

Section: Introductionmentioning

confidence: 99%

Recognition of pathological voices by Human Factor Cepstral Coefficients (HFCC)

Hamdi¹,

Hajji

Cherif

2020

Preprint

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Several tools have been introduced to achieve early detection of voice disorders. Among these tools are the human factor cepstral coefficients HFCC combined with prosodic parameters, the noise-harmonic ratio (NHR), the harmonic-noise ratio (HNR), analysis of trend fluctuations (DFA) and fundamental frequency (F0). These parameters are introduced and calculated in every frame. In this work, we used a variation of HFCC called equivalent rectangular bandwidth (ERB) to study the effects of HFCC on the classification of pathological voices. Using the HTK classifiers, the classification is carried out on two pathological databases, Massachusetts Eye and Ear Infirmary (MEEI) and Saarbruecken Voice Database (SVD). To assess the performance of the system, we used sensitivity and specificity.

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Voice pathology detection using auto-correlation of different filters bank

Cited by 19 publications

References 25 publications

Voice Pathology Detection Using Deep Learning: a Preliminary Study

Voice Pathology Detection Using Deep Learning: a Preliminary Study

Towards robust voice pathology detection

Recognition of pathological voices by Human Factor Cepstral Coefficients (HFCC)

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