Distributed TDNN-Fuzzy Vector Quantization for HMM speech recognition
Mohamed Debyeche, Aderrahmane Amrouche, Jean‐Paul Haton · 2009
This paper investigates the use of a time delay neural network (TDNN) as fuzzy vector quantizer to improve the distributed scheme of HMM speech recognition. We investigate how to optimize the use of the vector quantization (VQ) by combining complementary preprocessing techniques based on multi-streams acoustic analysis. Then, in order to eliminate the effect of quantization error incurred by the vector quantizer front-end process a distributed TDNN fuzzy vector quantizer (DTDNN-FVQ) scheme is proposed. The evaluation of the whole of these methods is performed by focusing on specific Arabic phonemes: emphatic and back consonants. Experimental results shows that the distributed approach proposed increases the global performance of the HMM speech recognition system.