Universal classification for hidden Markov models

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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>