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Automatic Speech Recognition System Based on Hybrid Feature Extraction Techniques Using TEO-PWP for in Real Noisy Environment


Wafa Helali, Zied Hajaiej and Adnen Cherif


Vol. 19  No. 10  pp. 118-124


Automatic speech recognition presents an interesting research area that has always attracted researchers to the general public. It is now giving rise to an important set of applications of a very varied nature and difficulty, involving millions of people around the world every day. In this paper, a model of speech recognition system in noisy environment is developed and analyzed. The proposed model relies on several hybrid feature extraction methods. Indeed, Teager-Energy Operator, Perceptual Wavelet Packet (TEO-PWP), Mel Cepstrum Coefficient (MFCC) and Perceptual Linear Production (PLP) are combined to construct a robust HMM based system. TIMIT database, which consist of both clean and noisy speech files recorded at different level of Speech-to-Noise Ratio (SNR, has been used for the system test. Results and observations are performed to prove the effectiveness of the proposed system relying on speech recognition rates.


Teager-Energy Operator TEO-PWP Enhancement Speech MFCC PLP RASTA-PLP HMM.