CRF-based stochastic pronunciation modeling for out-of-vocabulary spoken term detection
Dong Wang, Simon King, Nicholas Evans, Raphaël Troncy · 2010
Out-of-vocabulary (OOV) terms present a significant challenge to spoken term detection (STD). This challenge, to a large ex-tent, lies in the high degree of uncertainty in pronunciations of OOV terms. In previous work, we presented a stochastic pro-nunciation modeling (SPM) approach to compensate for this uncertainty. A shortcoming of our original work, however, is that the SPM was based on a joint-multigram model (JMM), which is suboptimal. In this paper, we propose to use con-ditional random fields (CRFs) for letter-to-sound conversion, which significantly improves quality of the predicted pronun-ciations. When applied to OOV STD, we achieve consider-able performance improvement with both a 1-best system and an SPM-based system. Index Terms: speech recognition, spoken term detection, con-ditional random field, joint multigram model