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...

Täydet tiedot

Bibliografiset tiedot

Julkaisussa:	IEEE Transactions on speech and audio processing 9, 3 (2001).
Päätekijä:	Bocchieri, E.
Aineistotyyppi:	Artikkeli
Kieli:	English
Aiheet:	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.