Speech recognition using an enhanced FVQ based on a codeword dependent distribution normalization and codeword weighting by fuzzy objective function

The paper presents a new variant of parameter estimation methods for discrete hidden Markov models (HMM) in speech recognition. This method makes use of a codeword dependent distribution normalization (CDDN) and a distance weighting by fuzzy contribution in dealing with the problems of robust state modeling in FVQ based modeling. The proposed method is compared with the existing techniques using speaker-independent phonetically balanced isolated word recognition. The results have shown that the recognition rate of the proposed method is improved 4.5% over the conventional NQ based method and the distance weighting to the smoothing of output probability is more efficient than the distance based codeword weighting