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Millions of children and adults suffer from acquired or congenital neuro-motor communication disorders that can affect their speech intelligibility. The automatically characterization of speech impairment can contribute to improve the patient's life quality, and assist experts in assessment and treatment design. In this paper, we present new approaches to improve the analysis and classification of disordered speech. First, we propose an automatic speaker recognition approach especially adapted to identify dysarthric speakers. Secondly, we suggest a method for the automatic assessment of the dysarthria severity level. For this purpose, a model simulating the external, middle and inner parts of the ear is presented. This ear model provides relevant auditory-based cues that are combined with the usual Mel-Frequency Cepstral Coefficients (MFCC) to represent atypical speech utterances. The experiments are carried out by using data of both Nemours and Torgo databases of dysarthric speech. Gaussian Mixture Models (GMMs), Support Vector Machines (SVMs) and hybrid GMM/SVM systems are tested and compared in the context of dysarthric speaker identification and assessment. The experimental results achieve a correct speaker identification rate of 97.2% which can be considered promising for this novel approach; also the existing assessment systems are outperformed with a 93.2% correct classification rate of dysarthria severity levels. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we are concerned with the problem of the adaptation of non-native speech in a large-vocabulary speech recognition system for Modern Standard Arabic (MSA). A technique to adapt Hidden Markov Models (HMMs) to foreign accents by using Genetic Algorithms (GAs) in unsupervised mode is presented. The implementation requirements of GAs, such as genetic operators and objective function, have been selected to give more reliability to a global linear transformation matrix. The Minimum Phone Error (MPE) criterion is used as an objective function. The West Point Language Data Consortium (LDC) modern standard Arabic database is used throughout our experiments. Results show that significant decrease of word error rate has been achieved by the evolutionary-based approach compared to conventional Maximum Likelihood Linear Regression (MLLR), Maximum a posteriori (MAP) techniques and to the adaptation combining MLLR and MPE-based training. [Copyright &y& Elsevier]