NSF Grant Number: IIS-0095940 PI: Joseph Picone Institution: Mississippi State University Title: Integrating Prosody, Speech Recognition, Parsing In Spoken-Language Research Objectives: Significant Results: Approach Graphic: Broader Impact: Better understand how integration of prosodic information, speech recognition and parsing can impact the problem of information extraction from spoken documents. Apart from disfluency cues, sentence-internal prosody speech does not provide strong cues for parsing (compared to punctuation) Methods based on machine learning and statistical modeling can be used to identify and correct speech repairs Four key themes in the proposed research: (1) utilizing parsing in information retrieval, (2) integrating prosodic information in parsing spoken language, (3) incorporating uncertainty in parsing to handle speech recognition errors, (4) improvements to speech recognition of spontaneous speech. All components share a probabilistic formulation. Initial steps towards information extraction from spoken documents or from any text (such as encyclopedias), and can serve as the basis for a sorely needed sophisticated web browser technology and data mining applications.