Automatic Derivation of a Phoneme Set with Tone Information for Chinese Speech Recognition Based on Mutual Information Criterion

An appropriate approach to model tone information is helpful for building Chinese large vocabulary continuous speech recognition system. We propose to derive an efficient phoneme set of tone-dependent sub-word units to build a recognition system, by iteratively merging a pair of tone-dependent units according to the principle of minimal loss of the mutual information. The mutual information is measured between the word tokens and their phoneme transcriptions in a training text corpus, based on the system lexical and language model. The approach has the capability to keep discriminative tonal (and phoneme) contrasts that are most helpful for disambiguating homophone words due to lack of tones, and merge those tonal (and phoneme) contrasts that are not important for word disambiguation for the recognition task. This enable a flexible selection of phoneme set according to a balance between the MI information amount and the number of phonemes. We applied the method to traditional phoneme set of initial/finals, and derived several phoneme sets with different number of units. Speech recognition experiments using the derived sets showed their effectiveness