Abstract
This paper investigates the contribution of formants and prosodic features like pitch and energy in Arabic speech recognition under real-life conditions. Our speech recognition system based on Hidden Markov Model (HMM) is implemented using the HTK Toolkit. The front-end of the system combines features based on conventional Mel-Frequency Cepstral Coefficient (MFFC), prosodic information and formants. The obtained results show that the resulting multivariate feature vectors lead to a significant improvement of the recognition system performance in noisy environment compared to cepstral system alone.
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