Author: SRI International

In this work we demonstrate the use of em prosodic cues, alone and in combination with words, for segmentation and name finding. In experiments, we find that prosodic cues alone allow sentence and topic segmentation that is at least as good as word-based methods alone, and that combining both types of cues gives significant wins.

We describe a new algorithm for finding the hypothesis in a recognition lattice that is expected to minimize the word error rate (WER). Our approach thus overcomes the mismatch between the word-based performance metric and the standard MAP scoring paradigm that is sentence-based, and that can lead to sub-optimal recognition results.

This paper addresses the issue of closed-set text-independent speaker identifcation from samples of speech recorded over the telephone. It focuses on the effects of acoustic mismatches between training and testing data, and concentrates on two approaches: extracting features that are robust against channel variations, and transforming the speaker models to compensate for channel effects.

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topic units. The approach combines hidden Markov models, statistical language models, and prosody-based decision trees.