Yvonne Moh

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Speaker clustering is a key component in many speech processing applications. We focus on Broadcast News meta data annotation and speaker adaptation. In this setting, speaker clustering consists of identifying who spoke, and when they spoke in a long news broadcast. Speaker clustering is given a set of short audio segments. Ideally, it will discover how(More)
We consider the problem of online learning in a changing environment under sparse user feedback. Specifically, we address the classification of music types according to a user's preferences for a hearing aid application. The classifier has to operate under limited computational resources. It must be capable of adjusting to types of data not represented in(More)
Personalization for real-world machine-learning applications usually has to incorporate user feedback. Unfortunately, user feedback often suffers from sparsity and possible inconsistencies. Here we present an algorithm that exploits feedback for learning only when it is consistent. The user provides feedback on a small subset of the data. Based on the data(More)
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