Recognition Using Classification and Segmentation Scoring*

Traditional statistical speech recognition systems typically make strong assumptions about the independence of obser-vation frames and generally do not make use of segmental information. In contrast, when the segmentation is known, existing classifiers can readily accommodate segmental infor-mation in the decision process. We describe an approach to connected word recognition that allows the use of segmental information through an explicit decomposition of the recog-nition criterion into classification and segmentation scoring. Preliminary experiments are presented, demonstrating that the proposed framework, using fixed length sequences of cep-stral feature vectors for classification of individual phonemes, performs comparably to more traditional recognition ap-proaches that use the entire observation sequence. We expect that performance gain can be obtained using this structure with additional, more general features. 1