Statistical signal processing using wavelet-domain hidden Markov models

1997 ◽  
Author(s):  
Matthew S. Crouse ◽  
Robert D. Nowak ◽  
Richard G. Baraniuk
Keyword(s):  
Signal Processing ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Statistical Signal Processing ◽  
Wavelet Domain

Speech Disorders Recognition using Speech Analysis

2013 ◽  
pp. 494-507 ◽  
Author(s):  
Khaled Necibi ◽  
Halima Bahi ◽  
Toufik Sari
Keyword(s):  
Signal Processing ◽  
Hidden Markov Models ◽  
Automatic Speech Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Speech Disorders ◽  
Speech Analysis ◽  
Physical Problems ◽  
Processing Techniques ◽  
Signal Processing Techniques

Speech disorders are human disabilities widely present in young population but also adults may suffer from such disorders after some physical problems. In this context, the detection and further the correction of such disabilities may be handled by Automatic Speech Recognition (ASR) technology. The first works on the speech disorders detection began early in the 70s and seem to follow the same evolution as those on the ASR. Indeed, these early works were more based on the signal processing techniques. Progressively, systems dealing with speech disorders incorporate more ideas from ASR technology. Particularly, Hidden Markov Models, the state-of-the-art approaches in ASR systems, are used. This chapter reviews systems that use ASR techniques to evaluate pronunciation of people who suffer from speech or voice impairments. The authors investigate the existing systems and present the main innovation and some of the available resources.

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