Background model based posterior probability for measuring confidence

Word posterior probability (WPP) computed over LVCSR word graphs has been used successfully in measuring confidence of speech recognition output. However, for certain applications the word graph is too sparse to warrant reliable WPP estimation. In this paper, we incorporate subword units as background models to generate a subword graph for estimating posterior probability. Experiments on both English and Chinese databases show that syllable background models can repopulate the dynamic hypothesis space for effective computation of confidence measure. The resultant posterior probability confidence measure achieves 94.3% and 95.2% Out-Of-Vocabulary (OOV) word detection / rejection in English and Chinese, respectively. Correspondingly, confidence error rates are at 6.0% and 6.4%, respectively.