Human/machine interface dialog integrating new information and communication technology for pathological voice

Zaidi, Brahim-Fares; Selouani, Sid‐Ahmed; Boudraa, Malika; Hamdani, Ghania

doi:10.1109/ftc.2016.7821653

Cited by 2 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each of these objectives is a challenge to overcome. Therefore, realizing a complex system that treats a large number of complications requires: The writing of several scripts (XML, HTML, PASCAL, and HTK) [16]- [18], manipulation and management of a large number of scripts lines, and the management of sound files, text and binary data.…”

Section:  Large Vocabularymentioning

confidence: 99%

Interface of an Automatic Recognition System for Dysarthric Speech

Zaidi¹,

Boudraa²,

Selouani³

et al. 2018

ijacsa

Self Cite

View full text Add to dashboard Cite

This paper addresses the realization of a Human/Machine (H/M) interface including a system for automatic recognition of the Continuous Pathological Speech (ARSCPS) and several communication tools in order to help frail people with speech problems (Dysarthric speech) to access services providing by new technologies of information and communication (TIC) while making it easier for the doctors to achieve a first diagnosis on the patient's disease. In addition, an ARSCPS has been improved and developed for normal and pathology voice while establishing a link with our graphic interface which is based on the box tools Hidden Markov Model Toolkit (HTK), in addition to the Hidden Models of Markov (HMM). In our work we used different techniques of feature extraction for the speech recognition system in order to improve the dysarthric speech intelligibility while developing an ARSCPS which can perform well for pathological and normal speakers. These techniques are based on the coefficients of ETSI standard Mel Frequency Cepstral Coefficient Front End (ETSI MFCC FE V2.0); Perceptual Linear Prediction coefficients (PLP); Mel Frequency Cepstral Coefficients (MFCC) and the recently proposed Power Normalized Cepstral Coefficients (PNCC) have been used as a basis for comparison. In this context we used the Nemours database which contains 11 speakers that represents dysarthric speech and 11 speakers that represents normal speech.

show abstract

Section:  Large Vocabularymentioning

confidence: 99%