Transition features for CRF-based speech recognition and boundary detection

Abstract

In this paper, we investigate a variety of spectral and time domain features for explicitly modeling phonetic transitions in speech recognition. Specifically, spectral and energy distance metrics, as well as, time derivatives of phonological descriptors and MFCCs are employed. The features are integrated in an extended Conditional Random Fields statistical modeling framework that supports general-purpose transition models. For evaluation purposes, we measure both phonetic recognition task accuracy and precision/recall of boundary detection. Results show that when transition features are used in a CRF-based recognition framework, recognition performance improves significantly due to the reduction of phone deletions. The boundary detection performance also improves mainly for transitions among silence, stop, and fricative phonetic classes.