Utterance verification using modified segmental probability model

Today speech recognition is requested not only to decode utterances into transcriptions, but also to determine the reliabilities of the result, by Utterance Verification (UV). With the conventional HMM, the measure of reliabilities can not be determined directly by the likelihoods of models. Whereas, Modified Segmental Probability Model (MSPM), suggested in this paper, with its normalized likelihood, facilitates rendering UV and speech recognition at the same time and as a whole. In the paper, Integrated Anti-word Model (IAM) is suggested, which is used to advance the measure of UV likelihood of MSPM. Some experiments show high perfor-mance and moderate computation with IAM.