Search results for: “elizabeth shriberg”
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Using Speech to Assess Post-Traumatic Stress Disorder in Veterans
Post-Traumatic Stress Disorder (PTSD) is a serious problem for the military – it affects at least 30 percent of military service members who have spent time in war zones. Not only does PTSD affect veterans, it also impacts their families and communities. Moreover, about eight percent of the civilian population is likely to develop PTSD at some point in…
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A Wizard of Oz framework for collecting spoken human-computer dialogs
This paper describes a data collection process aimed at gathering human-computer dialogs in high-stress or “busy” domains where the user is concentrating on tasks other than the conversation, for example, when driving a car.
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Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence.
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Spotting “Hot Spots” in Meetings: Human Judgments and Prosodic Cues
Recent interest in the automatic processing of meetings is motivated by a desire to summarize, browse, and retrieve important information from lengthy archives of spoken data. One of the most useful capabilities such a technology could provide is a way for users to locate “hot spots” or regions in which participants are highly involved in…
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The SRI March 2000 Hub-5 Conversational Speech Transcription System
We describe SRI’s large vocabulary conversational speech recognition system as used in the March 2000 NIST Hub-5E evaluation.
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Modeling Dynamic Prosodic Variation for Speaker Verification
In this work, we take a first step toward capturing suprasegmental patterns for automatic speaker verification. Prosody modeling improves the verification performance of a cepstrum-based Gaussian mixture model system (as measured by a task-specific Bayes risk) by 10%.
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Crosslinguistic Disfluency Modeling: A Comparative Analysis of Swedish and American English Human-Human and Human-Machine Dialogues
We report results from a cross-language study of disfluencies (DFs) in Swedish and American English human-machine and human-human dialogs. We focus on differences suggestive of how speakers utilize DFs in the different languages.
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Detection and Correction Of Repairs In Human-Computer Dialog
We present here criteria and techniques for automatically detecting the presence of a repair, its location, and making the appropriate correction. The criteria involve integration of knowledge from several sources: pattern matching, syntactic and semantic analysis, and acoustics.
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Automatic Detection and Correction of Repairs in Human-computer Dialog
We present here criteria and techniques for automatically detecting the presence of a repair, its location, and making the appropriate correction.