Automatic Recognition System for Dysarthric Speech Based on MFCC’s, PNCC’s, JITTER and SHIMMER Coefficients

The aim of this work is to improve the automatic recognition of the dysarthria speech. In this context, we have compared two techniques of speech parameterization; these two techniques are based on the recently proposed coefficients Power Normalized Cepstral Coefficients and Mel-Frequency Cepstral Coefficients. In this paper we have concatenate several variants of JITTER and SHIMMER with the techniques of speech parameterization to improve an automatic recognition of the dysarthric word system. The aim is to help the fragile persons having speech problems (dysarthric voice) and the doctor to make a first diagnosis about the patient’s disease. For this, an Automatic Acknowledgment of Continuous Pathological Speech System has been developed based on the Hidden Models of Markov and the Hidden Markov Model Toolkit. For our tests, we used the Nemours Database which contains 11 speakers representing dysarthric voices.

[1]  Virender Kadyan,et al.  Punjabi Automatic Speech Recognition Using HTK , 2012 .

[2]  Douglas D. O'Shaughnessy,et al.  Robust feature extractors for continuous speech recognition , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[3]  Jean Schoentgen,et al.  Time series analysis of jitter , 1995 .

[4]  Richard M. Stern,et al.  Power-Normalized Cepstral Coefficients (PNCC) for Robust Speech Recognition , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[5]  Max A. Little,et al.  Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity , 2011, Journal of The Royal Society Interface.

[6]  A. Aronson,et al.  Differential diagnostic patterns of dysarthria. , 1969, Journal of speech and hearing research.

[7]  H. Timothy Bunnell,et al.  The Nemours database of dysarthric speech , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[8]  Muhammad Ghulam,et al.  Automatic Speech Recognition of Pathological Voice , 2015 .

[9]  Sid-Ahmed Selouani,et al.  Human/machine interface dialog integrating new information and communication technology for pathological voice , 2016, 2016 Future Technologies Conference (FTC).

[10]  Mike Brookes,et al.  The DYPSA algorithm for estimation of glottal closure instants in voiced speech , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Mike Brookes,et al.  Estimation of Glottal Closure Instants in Voiced Speech Using the DYPSA Algorithm , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[12]  Athanasios Tsanas,et al.  Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear speech signal processing and statistical machine learning , 2012 .