Robust Arabic Multi-stream Speech Recognition System in Noisy Environment

In this paper, the framework of multi-stream combination has been explored to improve the noise robustness of automatic speech recognition systems. The main important issues of multi-stream systems are which features representation to combine and what importance (weights) be given to each one. Two stream features have been investigated, namely the MFCC features and a set of complementary features which consists of pitch frequency, energy and the first three formants. Empiric optimum weights are fixed for each stream. The multi-stream vectors are modeled by Hidden Markov Models (HMMs) with Gaussian Mixture Models (GMMs) state distributions. Our ASR is implemented using HTK toolkit and ARADIGIT corpus which is data base of Arabic spoken words. The obtained results show that for highly noisy speech, the proposed multi-stream vectors leads to a significant improvement in recognition accuracy.

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