COMBINING WORDS AND PROSODY FOR INFORMATION EXTRACTION FROM SPEECH

Information extraction from speech is a crucial step on the way from speech recognition to speech understanding. A preliminary step toward speech understanding is the detection of topic boundaries, sentence boundaries, and proper names in speech recognizer output. This is important since speech recognizer output lacks the usual textual cues to these entities (such as headers, paragraphs, sentence punctuation, and capitalization). Numerous word-based approaches to these tasks have been developed in the past; in this work we demonstrate the use of prosodic cues, alone and in combination with words, for segmentation and name finding. In experiments on the Broadcast News corpus, 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. Named entity recognition, on the other hand, currently does not seem to benefit from prosodic cues, for several interesting reasons.