Investigation of model‐based error rate estimation techniques for speaker‐independent large‐vocabulary recognition tasks

Abstract

This paper proposes model‐based error rate estimation techniques for HMM‐based speech recognition systems and shows their effectiveness in several speaker‐independent task conditions. The proposed techniques are based on an HMM‐based error rate estimation method [C.‐S. Huang et al., IEEE Trans. SAP, 11, No. 6, 581–589 (2003)], which only depends on subword‐based pronunciation lexicon knowledge. This study assumes the need of the dynamic change of open vocabulary. The vocabulary size is varied between 5000 and 25 000 words. Two types of formalizations are investigated. They utilize the Bhattacharyya distance as a local distance measure between two competing word classes which are modeled by syllable‐unit HMMs. A misclassification measure, which is similar to the one used by minimum classification error (MCE) training methods, is used for estimating error rate. The correlation between the error rates for testing utterances by 20 speakers and the estimated error rates for varied vocabulary sizes are evalu...