4th International Conference on Spoken Language Processing

Philadelphia, PA, USA
October 3-6, 1996

Speech Recognition Using an Enhanced FVQ Based on a Codeword Dependent Distribution Normalization and Codeword Weighting by Fuzzy Objective Function

Hwan Jin Choi, Yung Hwan Oh

Department of Computer Science, Korea Advanced Institute of Science and Technology, Taejon, Korea

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 a FVQ based modeling. The proposed method is compared with the existing techniques using speaker-independent phonetically balanced isolated words recognition. The results have shown that the recognition rate of the proposed method is improved 4.5% over the conventional FVQ based method and the distance weighting to the smoothing of output probability is more efficient than the distance based codeword weighting.

Full Paper

Bibliographic reference.  Choi, Hwan Jin / Oh, Yung Hwan (1996): "Speech recognition using an enhanced FVQ based on a codeword dependent distribution normalization and codeword weighting by fuzzy objective function", In ICSLP-1996, 354-357.