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• W2009388533 abstract "Zero-resource speech processing involves the automatic analysis of a collection of speech data in a completely unsupervised fashion without the benefit of any transcriptions or annotations of the data. In this paper, our zero-resource system seeks to automatically discover important words, phrases and topical themes present in an audio corpus. This system employs a segmental dynamic time warping (S-DTW) algorithm for acoustic pattern discovery in conjunction with a probabilistic model which treats the topic and pseudo-word identity of each discovered pattern as hidden variables. By applying an Expectation-Maximization (EM) algorithm, our system estimates the latent probability distributions over the pseudo-words and topics associated with the discovered patterns. Using this information, we produce acoustic summaries of the dominant topical themes of the audio document collection." @default.
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• W2009388533 date "2013-05-01" @default.
• W2009388533 modified "2023-09-25" @default.
• W2009388533 title "Zero resource spoken audio corpus analysis" @default.
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• W2009388533 doi "https://doi.org/10.1109/icassp.2013.6639335" @default.
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