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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Amrous, A.I., Debyeche, M., Amrouche, A. (2011). Prosodic Features and Formant Contribution for Arabic Speech Recognition in Noisy Environments. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19644-7_49
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DOI: https://doi.org/10.1007/978-3-642-19644-7_49
Publisher Name: Springer, Berlin, Heidelberg
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