Invited: SVMS, Score-Spaces and Maximum Margin Statistical Models (国際ワークショップ"Beyond HMM")

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概要

• 論文の詳細を見る

• There has been significant interest in developing new forms of acoustic model, in particular models which allow additional dependencies to be represented than allowed within a standard hidden Markov model (HMM). This paper discusses one such class of models, augmented statistical models. Here a locally exponential approximation is made about some point on a base distribution. This allows additional dependencies within the data to be modelled than are represented in the base distribution. Augmented models based on Gaussian mixture models (GMMs) and HMMs are briefly described. These augmented models are then related to generative kernels, one approach used for allowing support vector machines (SVMs) to be applied to variable length data. The training of augmented statistical models within an SVM, generative kernel, framework is then discussed. This may be viewed as using maximum margin training to estimate statistical models. Augmented Gaussian mixture models are then evaluated using rescoring on a large vocabulary speech recognition task.

• 一般社団法人情報処理学会の論文
• 2004-12-20

著者

• Layton M.

  Engineering Department, Cambridge University

• Gales M.j.f

  Engineering Department Cambridge University

• Layton M.

  Engineering Department Cambridge University

• Gales M.

  Engineering Department, Cambridge University

関連論文

• SVMS, SCORE-SPACES AND MAXIMUM MARGIN STATISTICAL MODELS
• SVMS, SCORE-SPACES AND MAXIMUM MARGIN STATISTICAL MODELS
• Invited: SVMS, Score-Spaces and Maximum Margin Statistical Models (国際ワークショップ"Beyond HMM")

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