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ROBUST SPEECH RECOGNITION USING FUZZY MATRIX QUANTISATION , NEURAL NETWORKS AND HIDDEN MARKOV MODELS Professor

Both systems provide significant benefits in recognition accuracy, at low SNR input signal conditions, when compared to conventional previously reported methods ([1] to [4]). This robust performance ensures that a typical recognition accuracy of the order of 93% and 85% is obtained at input SNR values of 5 dB and 0 dB respectively. In contrast, under the same conditions, conventional systems can manage only a poor 55% and 37%. The theory and structure of the proposed relatively small vocabulary, SD-IWSR schemes is presented in the paper together with recognition performance computer simulation results based on extensive tests. In this paper a new approach to robust speech recognition using Fuzzy Matrix Quantisation, Hidden Markov Models and Neural Networks is presented and tested when speech is corrupted by car noise. Thus two new robust isolated word speech recognition (IWSR) systems called FMQ/HMM and FMQ/MLP, are proposed and designed optimally for operation in a variety of input SNR conditions. The schemes and associated system training methodologies result into a particularly high recognition performance at input SNR levels as low as 5 and 0 dBs.