Confidence Scoring for Accurate HMM-Based Speech Recognition by Using Monophone-Level Normalization Based on Subspace Method (<Special Issue>Special Issue on Speech Information Processing)

概要

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In this paper, a novel confidence scoring method that is applied to N-best hypotheses (word candidates) output from an HMM-based classifier is proposed. In the first pass of the proposed method, the HMM-based classifier with monophone models outputs N-best hypotheses and boundaries of all monophones in the hypotheses. In the second pass, an SM (Subspace Method)-based verifier tests the hypotheses by comparing confidence scores. To test the hypotheses, at first, the SM-based verifier calculates the similarity between phone vectors and an eigen vector set of monophones, then this similarity score is converted into a likelihood score with normalization of acoustic quality, and finally, an HMM-based likelihood of word level and an SM-based likelihood of monophone level are combined to formulate the confidence measure. Two kinds of experiments were performed to evaluate this confidence measure on speaker-independent word recognition. The results showed that the proposed confidence scoring method significantly reduced the word error rate from 4.7% obtained by the standard HMM classifier to 2.0%, and in an unknown word rejection, it reduced the equal error rate from 9.0% to 6.5%.
社団法人電子情報通信学会の論文
2003-03-01