Mixture distributions are a particular type of conditional density with appealing modeling properties. Under a special case of segment models using variable-length representations and conditional densities, various forms of Gaussian mixture models are examined for the individual samples of the feature sequence. Within this framework, a systematic comparison of both existing and novel mixture modeling techniques is conducted. Parameter-tying alternatives for frame-level mixtures are explored and good performance is demonstrated with this approach.
Within the conditional-density variable-length framework, a generalization of mixture distributions that captures properties of the complete segment is proposed in the form of a segment-level mixture model. This approach models intra-segment correlation indirectly using a mixture of segment-length models, each of which uses conditionally independent time samples. Parameter estimation formulae are derived and the model is explored experimentally.
The alternative assumption of modeling based on a posteriori probabilities is examined through the development of a recognition formalism using classification and segmentation scoring. Posterior distributions have been less well studied than conditional densities in the context of CSR, and this work introduces a theoretically consistent, segment-level posterior distribution model using context-dependent models. Issues concerning fixed versus variable-length representations and segmentation scoring are explored experimentally. Finally, some general conclusions are drawn concerning the practical and theoretical trade-offs for the models examined.
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