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In this paper, we present an unsupervised learning framework to address the problem of detecting spoken keywords. Without any transcription information, a Gaussian Mixture Model is trained to label sp...
We present a novel approach to speech processing based on the principle of pattern discovery. Our work represents a departure from traditional models of speech recognition, where the end goal is to cl...
We address the task of unsupervised topic segmentation of speech data operating over raw acoustic information. In contrast to existing algorithms for topic segmentation of speech, our approach does no...
In this paper, we present an unsupervised method for automatically discovering words from speech using a combination of acoustic pattern discovery, graph clustering, and baseform searching. The algori...

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