A new decoder based on a generalized confidence score

Myoung-Wan Koo, Chin-Hui Lee, Biing-Hwang Juang

Abstract: We propose a new decoder based on a generalized confidence score. The generalized confidence score is defined as a product of confidence scores obtained from confidence information sources such as likelihood, likelihood ratio, duration, duration ratio, language model probabilities, supra-segmental information, etc. All confidence information sources are converted into confidence scores by a confidence pre-processor. We show an extended hybrid decoder as an example of the decoder based on the generalized confidence score. The extended hybrid decoder uses multi-level confidence scores such as frame-level, phone-level, and word-level likelihood ratios, while the conventional hybrid decoder uses the frame-level confidence score. The experimental result shows that the extended decoder gives a better result than the conventional hybrid decoder, particularly in dealing with out-of-vocabulary words or out-of-task sentences.