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Clustering algorithms such as k-means perform poorly when the data is highdimensional. A number of efficient clustering algorithms developed in recent years address this problem by projecting the data into a lower-dimensional subspace, e.g. via principal components analysis (PCA) or random projectio ...

Since the 1980s, the main approach to automatic speech recognition has been using hidden Markov models (HMMs), in which each state corresponds to a phoneme or part of a phoneme in the context of the neighboring phonemes. Despite their crude approximation of the speech signal, and the large margin fo ...

Spoken language technologies, such as automatic speech recognition and synthesis, typically treat speech as a string of "phones". In contrast, humans produce speech through a complex combination of semi-independent articulatory trajectories. Recent theories of phonology acknowledge this, and treat s ...