An initial study on a segmental probability model approach to large-vocabulary continuous Mandarin speech recognition

This paper presents an initial study to perform large-vocabulary continuous Mandarin speech recognition based on a segmental probability model (SPM) approach. SPM was first proposed for recognition of isolated Mandarin syllables, in which every syllable must be equally segmented before recognition. A concatenated syllable matching algorithm is therefore introduced in place of the conventional Viterbi search algorithm to perform the recognition process based on SPM. In addition, a training procedure is also proposed to reestimate the SPM parameters for continuous speech. Preliminary simulation results indicate that significant improvements in both recognition rates and speed can be achieved as compared to the conventional HMM-based Viterbi search approaches