SUMMARY This paper suggests word voiceprint models to verify the recognition results obtained from a speech recognition system. Word voiceprint models have word-dependent information based on the distributions of phone-level log-likelihood ratio and duration. Thus, we can obtain a more reliable confidence score for a recognized word by using its word voiceprint models that represent the more proper characteristics of utterance verification for the word. Additionally, when obtaining a loglikelihood ratio-based word voiceprint score, this paper proposes a new log-scale normalization function using the distribution of the phone-level log-likelihood ratio, instead of the sigmoid function widely used in obtaining a phone-level log-likelihood ratio. This function plays a role of emphasizing a mis-recognized phone in a word. This individual information of a word is used to help achieve a more discriminative score against out-ofvocabulary words. The proposed method requires additional memory, but it shows that the relative reduction in equal error rate is 16.9% compared to the baseline system using simple phone log-likelihood ratios.

SUMMARYThis paper suggests utterance verification system using state-level log-likelihood ratio with frame and state selection. We use hidden Markov models for speech recognition and utterance verification as acoustic models and anti-phone models. The hidden Markov models have three states and each state represents different characteristics of a phone. Thus we propose an algorithm to compute state-level log-likelihood ratio and give weights on states for obtaining more reliable confidence measure of recognized phones. Additionally, we propose a frame selection algorithm to compute confidence measure on frames including proper speech in the input speech. In general, phone segmentation information obtained from speaker-independent speech recognition system is not accurate because triphone-based acoustic models are difficult to effectively train for covering diverse pronunciation and coarticulation effect. So, it is more difficult to find the right matched states when obtaining state segmentation information. A state selection algorithm is suggested for finding valid states. The proposed method using state-level log-likelihood ratio with frame and state selection shows that the relative reduction in equal error rate is 18.1 % compared to the baseline system using simple phone-level log-likelihood ratios.