Minimum Bayes Risk Estimation and Decoding in Large Vocabulary Continuous Speech Recognition(<Special Section> Statistical Modeling for Speech Processing)

スポンサーリンク

概要

• 論文の詳細を見る

• Minimum Bayes risk estimation and decoding strategies based on lattice segmentation techniques can be used to refine large vocabulary continuous speech recognition systems through the estimation of the parameters of the underlying hidden Markov models and through the identification of smaller recognition tasks which provides the opportunity to incorporate novel modeling and decoding procedures in LVCSR. These techniques are discussed in the context of going 'beyond HMMs', showing in particular that this process of subproblem identification makes it possible to train and apply small-domain binary pattern classifiers, such as Support Vector Machines, to large vocabulary continuous speech recognition.

• 社団法人電子情報通信学会の論文
• 2006-03-01

著者

• Byrne William

  The Cambridge University Engineering Department

関連論文

• Minimum Bayes Risk Estimation and Decoding in Large Vocabulary Continuous Speech Recognition( Statistical Modeling for Speech Processing)

スポンサーリンク