IMPLEMENTATION OF HIERARCHICAL PHONEME CLASSIFICATION APPROACH ON LTDIGITS CORPORA

Abstract

Better discrimination of phonemic units still remains one of the most important problems in automatic speech recognition. Direct phoneme recognition in speaker independent automatic speech recognition systems is unable to provide good enough recognition results. There is made an assumption that better results could be achieved through the recognition of phoneme groups using group characteristic features: voiced/unvoiced, vowel/consonant, etc. This paper describes and statistically motivates features and rules for the detection of phoneme groups using phonetically labeled data. Algorithms for recognition of stop and fricative consonants are presented. Experimental research confirmed the advantages of the hierarchical classification of phonemes. Combination of knowledge and rules for detection of acoustic events with the classical statistical classification methods produced an overall 3% improvement of phoneme recognition accuracy and a 52-55% reduction of time taken by classification.