Representing dynamic features of phonetic segment in an orthogonalized codebook of HMM based speech recognition system

The authors propose a matrix quantization (MQ) algorithm named statistical MQ (SMQ) which uses an orthogonalized phonetic segment codebook. The SMQ effectively incorporates pattern variations of each phonetic segment into the orthogonalized phonetic segment codebook, and transforms an input speech to a sequence of phonetic symbols which include about 700 types of phonetic segments. The authors also propose a simple SMQ-HMM training algorithm called an equally counted K-based learning in which each phonetic event observed within the best K is equally counted in a model and output probabilities are smoothed without fuzzy rule. The proposed algorithm has been tested on a 546-word vocabulary data set uttered by 10 unknown speakers, using a real time recognition system, and has achieved the high performance of 96.5%