Bayesian estimation of keyword confidence in Chinese continuous speech recognition

In a syllable-based speaker-independent Chinese continuous speech recognition system based on classical Hidden Markov Model (HMM), a Bayesian approach of keyword confidence estimation is studied, which utilizes both acoustic layer scores and syllable-based statistical language model (LM) scores. The Maximum A Posteriori (MAP) confidence measure is proposed, and the forward-backward algorithm calculating the MAP confidence scores is deduced. The performance of the MAP confidence measure is evaluated in keyword spotting application and the experiment results show that the MAP confidence scores provide high discriminability for keyword candidates. Furthermore, the MAP confidence measure can be applied to various speech recognition applications.