Abstract
We investigate whether probabilistic modeling of prosody can aid various automatic labeling tasks essential for processing of multi-party meetings. Task 1, automatic punctuation, seeks to classify sentence boundaries and disfluencies. Task 2, jump-in points, predicts locations within foreground speech at which background speakers start talking; Task 3, jump-in words, examines characteristics of the speech they use to do so. Data are from the ICSI Meeting Recorder corpus. To infer inherent cues, analyses are based on close-talking microphone signals and recognizer forced alignments. As a generous baseline for word-level cues, we compare prosodic models to those of a language model given the true words. […]
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