A feature-based hierarchical speech recognition system for Hindi

Abstract

This paper presents a description of a speech recognition system for Hindi. The system follows a hierarchic approach to speech recognition and integrates multiple knowledge sources within statistical pattern recognition paradigms at various stages of signal decoding. Rather than make hard decisions at the level of each processing unit, relative confidence scores of individual units are propagated to higher levels. Phoneme recognition is achieved in two stages: broad acoustic classification of a frame is followed by fine acoustic classification. A semi-Markov model processes the frame level outputs of a broad acoustic maximum likelihood classifier to yield a sequence of segments with broad acoustic labels. The phonemic identities of selected classes of segments are decoded by class-dependent neural nets which are trained with class-specific feature vectors as input. Lexical access is achieved by string matching using a dynamic programming technique. A novel language processor disambiguates between multiple choices given by the acoustic recognizer to recognize the spoken sentence.