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Publications (1)0 Total impact

  • Anissa Imen Amrous, Mohamed Debyeche, Abderrahman Amrouche
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    ABSTRACT: This paper investigates the contribution of formants and prosodic features such as pitch and energy in Arabic speech recognition under real-life conditions. Our speech recognition system based on Hidden Markov Models (HMMs) 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 experiments are performed on the ARADIGIT corpus which is a database of Arabic spoken words. The obtained results show that the resulting multivariate feature vectors, in noisy environment, lead to a significant improvement, up to 27%, in word accuracy relative the word accuracy obtained from the state-of-the-art MFCC-based system. KeywordsASR system–HMM–MFCC–Formant–Prosodic features–Speech variability–Additive noise
    International Journal of Speech Technology 01/2011; 14(4):351-359.