A genetic model for acoustic and phonetic decoding of standard arabic vowels in continuous speech

This paper presents an Acoustic and Phonetic Decoding Model (APDM) for automatic recognition Standard Arab (S.A) plain (pure) and emphatic vowels sounds of continuous naturally spoken speech, using Genetic Algorithms (G.As). SA vowels were selected since they are the most difficult phonemes to recognize. We have used GAs because of their advantages in resolving complicated optimization problems and because the results are obtained more rapidly than in the classical methods. In addition, the computational cost is greatly reduced. Our Genetic APDM performs automatically and in parallel, the operation of concatenations of short-term parametric vectors during the speech continuum segmentation stage, and the classification of the acoustic segments of continuous and natural speech into different vowel classes. In order to perform our task, we have used both the Mel Frequency Cepstrum Coding (MFCC) and the Linear Prediction Coding (LPC) methods to extract vocal tract parametric coefficients from the speech signal successfully. Among a set of classifiers we have used the distance one. This paper explains how we have used the Manhattan distance as decision rule the GA evaluation to classify the discriminate parameters vectors. The analysed corpus contains hundreds of sentences composed of the all types of SA vowels in different contexts and recorded by several Algerian male and female speakers, in quite noisy environment. The Corpus phonemes were classified successfully with an overall average rate of 98.02%.