Robust Arabic speech recognition in noisy environments using prosodic features and formant

Authors:
Anissa Imen Amrous;Mohamed Debyeche;Abderrahman Amrouche
Affiliations:
Speech Communication and Signal Processing Laboratory (LPCTS), Faculty of Electronics and Computer Sciences, USTHB, Algiers, Algeria;Speech Communication and Signal Processing Laboratory (LPCTS), Faculty of Electronics and Computer Sciences, USTHB, Algiers, Algeria;Speech Communication and Signal Processing Laboratory (LPCTS), Faculty of Electronics and Computer Sciences, USTHB, Algiers, Algeria
Venue:
International Journal of Speech Technology
Year:
2011

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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.