Study of acoustic analysis and modeling for Automatic Standard Arabic Speech Recognition

In this paper, we are interested in the development of an HMM-based Automatic Speech Recognition (ASR) system dedicated to Standard Arabic (SA) language. The acoustical analysis is one of the most delicate tasks in an ASR system development. In order to guarantee enough informative acoustic parameters describing each audio frame, we made a first study on frame windowing (size and period) and a second study on features extraction methods traditionally used in ASR such as MFCC (Melscale Frequency Cepstral Coefficients) and PLP (Perceptual Linear Prediction). This second study allowed us to select the most appropriate coefficients number describing each audio frame. Modeling acoustic models is a key step in any ASR system. That‟s why, we analyzed the effect of varying Gaussian number per HMM state and the number of embedded re-estimations of the Baum-Welch algorithm. To evaluate the proposed ASR system, a multi-speaker SA connected-digits corpus is collected, transcribed and used throughout all experiments. This paper concludes by evaluating our ASR system on a speaker-independent continue SA speech corpus. The phonemes recognition rate is about 94% which is relatively high when comparing it with another ASR system evaluated on the same corpus.