Sentence boundary detection in chinese broadcast news using conditional random fields and prosodic features

In this paper, we explore the use of prosodic features in sen-tence boundary detection in Chinese broadcast news. The prosodic features include speaker turn, music, pause dura-tion, pitch, energy and speaking rate. Specifically, consider-ing the Chinese tonal effects in pitch trajectory, we propose to use tone-normalized pitch features. Experiments using deci-sion trees demonstrate that the tone-normalized pitch features show superior performance in sentence boundary detection in Chinese broadcast news. Furthermore, feature combination is able to achieve apparent performance improvement by in-tuitive feature interactive rules formed in the decision tree. Pause duration and a tone-normalized pitch feature contribute the most part of the feature usage in the best-performing de-cision tree. Index Terms — sentence boundary detection, sentence segmentation, speech prosody, rich transcription 1

Using Prosody for Automatic Sentence Segmentation of Multi-Party Meetings

We explore the use of prosodic features beyond pauses, including duration, pitch, and energy features, for automatic sentence segmentation of ICSI meeting data. We examine two different approaches to boundary classification: score-level combination of independent language and prosodic models using HMMs, and feature-level combination of models using a boosting-based method (BoosTexter). We report classification results for reference word transcripts as well as for transcripts from a state-of-the-art automatic speech recognizer (ASR). We also compare results using the lexical model plus a pause-only prosody model, versus results using additional prosodic features. Results show that (1) information from pauses is important, including pause duration both at the boundary and at the previous and following word boundaries; (2) adding duration, pitch, and energy features yields significant improvement over pause alone; (3) the integrated boosting-based model performs better than the HMM for ASR conditions; (4) training the boosting-based model on recognized words yields further improvement