Title :
Universal classification for hidden Markov models
Author_Institution :
AT&T Bell Lab., Murray Hill, NJ, USA
fDate :
11/1/1991 12:00:00 AM
Abstract :
Binary hypotheses testing using empirically observed statistics is studied in the Neyman-Pearson formulation for the hidden Markov model (HMM). An asymptotically optimal decision rule is proposed and compared to the generalized likelihood ratio test (GLRT), which has been shown in earlier studies to be asymptotically optimal for simpler parametric families. The proof of the main theorem is provided. The result can be applied to several types of HMMs commonly used in speech recognition and communication applications. Several applications are demonstrated
Keywords :
Markov processes; data compression; information theory; speech recognition; HMM; Neyman-Pearson formulation; asymptotically optimal decision rule; binary hypothesis testing; communication applications; generalized likelihood ratio test; hidden Markov model; speech recognition; universal classification; Digital communication; Helium; Hidden Markov models; Parametric statistics; Radar applications; Signal detection; Sonar; Speech recognition; Statistical analysis; System testing;
Journal_Title :
Information Theory, IEEE Transactions on
