On efficient Viterbi decoding for hidden semi-Markov models
Ritendra Datta, Jianying Hu, et al.
ICPR 2008
A standard approach to automatic speech recognition uses hidden Markov models whose state dependent distributions are Gaussian mixture models. Each Gaussian can be viewed as an exponential model whose features are linear and quadratic monomials in the acoustic vector. We consider here models in which the weight vectors of these exponential models are constrained to lie in an affine subspace shared by all the Gaussians. This class of models includes Gaussian models with linear constraints placed on the precision (inverse covariance) matrices (such as diagonal covariance, maximum likelihood linear transformation, or extended maximum likelihood linear transformation), as well as the LDA/HLDA models used for feature selection which tie the part of the Gaussians in the directions not used for discrimination. In this paper, we present algorithms for training these models using a maximum likelihood criterion. We present experiments on both small vocabulary, resource constrained, grammar-based tasks, as well as large vocabulary, unconstrained resource tasks to explore the rather large parameter space of models that fit within our framework. In particular, we demonstrate significant improvements can be obtained in both word error rate and computational complexity. © 2005 IEEE.
Ritendra Datta, Jianying Hu, et al.
ICPR 2008
Dorit Nuzman, David Maze, et al.
SYSTOR 2011
Arnon Amir, M. Lindenbaum
Computer Vision and Image Understanding
Kuan-Yu Chen, Shih-Hung Liu, et al.
EMNLP 2014