Dimensionality reduction for voice disorders identification system based on Mel Frequency Cepstral Coefficients and Support Vector Machine

Souissi, Nawel; Cherif, Adnane

doi:10.1109/icmic.2015.7409479

Cited by 28 publications

(17 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our future work will build on current experiment, but we will limit the number of pathologies only to those having the most samples as in [2], [8]- [10] and we will train separate models for males and females as in [15]. We will investigate whether training with combination of vowels /a/, /i/ and /u/ help to improve the accuracy as in [7], [12], [15].…”

Section: Resultsmentioning

confidence: 99%

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)

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

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)

View full text Add to dashboard Cite

show abstract

“…Among several machine learning techniques existing in literature, Support Vector Machine (SVM) has been widely used in voice signal processing such as the work of L.Godino [7] and S.N. [8] with accuracy ratio of 86%.…”

Section: Related Workmentioning

confidence: 99%

“…[5,6,7,8,23,24]. Although in these works the databases are different, it is observed that the proposed algorithm with HMM appears competitive and has a high accuracy to identify pathological and normal voices.…”

Section: E Pathology Recognition Ratiomentioning

confidence: 99%

A New E-Health Tool for Early Identification of Voice and Neurological Pathologies by Speech Processing

Bouafif¹,

Ellouze²

2018

ijacsa

View full text Add to dashboard Cite

Abstract-The objective of this study is to develop a noninvasive method of early identification and classification of voice pathologies and neurological diseases by speech processing. We will present a new automatic medical diagnosis tool which can assist specialists in their medical diagnosis. The developed strategy is based on speech acquisition of the patient followed by audio features extraction, training and recognition by using the HTK toolkit. The computed parameters are compared to standard values from a codebook database. The experiments and tests are conducted by using the MEEI pathological database of KEY Pentax. The obtained results give good discrimination with a mean pathology recognition ratio about 95%. Finally, this EHealth application is helpful for the prevention of specific diseases and improving the quality of patient care as well as reducing the costs of healthcare.

show abstract

“…Objective techniques for voice screening have been proposed in recent studies based on time-domain [8], spectral [9] and cepstral analysis [10], [11]. These techniques include the use of amplitude, pitch, Mel-cepstral frequency, perturbationshimmer, perturbation-jitter and harmonic to noise ratio.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Speech Disorder in Dysarthria using Extended Speech Feature Extraction and Neural Networks Classification

Ijitona¹,

Soraghan²,

Lowit³

et al. 2017

IET 3rd International Conference on Intelligent Signal Processing (ISP 2017)

View full text Add to dashboard Cite

This paper presents an automatic detection of Dysarthria, a motor speech disorder, using extended speech features called Centroid Formants. Centroid Formants are the weighted averages of the formants extracted from a speech signal. This involves extraction of the first four formants of a speech signal and averaging their weighted values. The weights are determined by the peak energies of the bands of frequency resonance, formants. The resulting weighted averages are called the Centroid Formants. In our proposed methodology, these centroid formants are used to automatically detect Dysarthric speech using neural network classification technique. The experimental results recorded after testing this algorithm are presented. The experimental data consists of 200 speech samples from 10 Dysarthric speakers and 200 speech samples from 10 age-matched healthy speakers. The experimental results show a high performance using neural networks classification. A possible future research related to this work is the use of these extended features in speaker identification and recognition of disordered speech.

show abstract

Dimensionality reduction for voice disorders identification system based on Mel Frequency Cepstral Coefficients and Support Vector Machine

Cited by 28 publications

References 6 publications

Voice Pathology Detection Using Deep Learning: a Preliminary Study

Voice Pathology Detection Using Deep Learning: a Preliminary Study

A New E-Health Tool for Early Identification of Voice and Neurological Pathologies by Speech Processing

Automatic Detection of Speech Disorder in Dysarthria using Extended Speech Feature Extraction and Neural Networks Classification

Contact Info

Product

Resources

About