In this project we investigate Kernel Methods for sequence analy-sis. We concentrate on two major machine learning tasks for se-quence analysis – Unsupervised and Supervised learning. The first one concentrates on learning hidden structures in the data whereas the second task concentrates on classification. Two crucial prob-lems in speech processing is explored in this context – Speaker diarization (Unsupervised learning) and Automatic Speech Recog-nition (ASR). Results show that these alternative processing tech-niques have significant potential
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