Subspace distribution clustering hidden Markov model.

Most contemporary laboratory recognizers require too much memory to run, and are too slow for mass applications. One major cause of the problem is the large parameter space of their acoustic models. In this paper, we propose a new acoustic modeling methodology which we call subspace distribution clu...

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書誌詳細

出版年:	IEEE Transactions on speech and audio processing 9, 3 (2001).
第一著者:	Bocchieri, E.
フォーマット:	論文
言語:	English
主題:	ATIS task. Airline Travel Information System. CDHMM. SDCHMM. Acoustic models. Combinatorial effect. Continuous density hidden Markov models. Full-space distributions. Memory requirement. Orthogonal subspaces. Subspace distribution clustering hidden Markov model. Subspace distribution encoding.