Speaker segmentation for browsing recorded audio
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This paper describes a method for exploiting prosodic information in natural, conversational speech for the purpose of automatically creating an audio summary. The method is based on identifying emphasized speech and then using proximity measures on the emphasized regions to select summarizing excerpts. Emphasized speech is recognized using a hidden Markov model employing only non-spectral, prosodic information. Syllable-based models were created and the models trained on spontaneous speech in which words had been labeled by a panel of listeners for degree of emphasis. Emphatic speech from one speaker was automatically detected and summarizing excerpts were identified, with no noticeable difference when compared to excerpts selected by individual subjects. The extensibility of the emphasis detector to other speakers was tested on a small sample of telephone speech by 10 other speakers.