Modeling linguistic segment and turn boundaries for n-best rescoring of spontaneous speech
Andreas Stolcke · 1997
Language modeling, especially for spontaneous speech, often suffers from a mismatch of utterance segmentations between training and test conditions. In particular, training often uses linguistically-based segments, whereas testing occurs on acoustically determined segments, resulting in degraded performance. We present an N-best rescoring algorithm that removes the effect of segmentation mismatch. Furthermore, we show that explicit language modeling of hidden linguistic segment boundaries is improved by including turn-boundary events in the model. 1. THE SEGMENTATION PROBLEM IN LANGUAGE MODELING One of the problems encountered in speech recognition on continuous, spontaneous speech is the segmentation of long waveforms. Because current recognizers prefer short waveform segments for best performance and to limit computational resources, conversation-length waveforms are typically pre-segmented using simple acoustic criteria, such as locations of long pauses and turn switches. This crea...