Robust speech recognition using fuzzy matrix quantisation, neural networks and Hidden Markov models

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.