By Topic

N-Best rescoring by adaboost phoneme classifiers for isolated word recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Hiroshi Fujimura ; Corporate Research and Development Center, Toshiba Corporation, 1, Komukai-Toshiba-cho, Saiwai-ku, Kawasaki, 212-8582, Japan ; Masanobu Nakamura ; Yusuke Shinohara ; Takashi Masuko

This paper proposes a novel technique to exploit generative and discriminative models for speech recognition. Speech recognition using discriminative models has attracted much attention in the past decade. In particular, a rescoring framework using discriminative word classifiers with generative-model-based features was shown to be effective in small-vocabulary tasks. However, a straightforward application of the framework to large-vocabulary tasks is difficult because the number of classifiers increases in proportion to the number of word pairs. We extend this framework to exploit generative and discriminative models in large-vocabulary tasks. N-best hypotheses obtained in the first pass are rescored using AdaBoost phoneme classifiers, where generative-model-based features, i.e. difference-of-likelihood features in particular, are used for the classifiers. Special care is taken to use context-dependent hidden Markov models (CDHMMs) as generative models, since most of the state-of-the-art speech recognizers use CDHMMs. Experimental results show that the proposed method reduces word errors by 32.68% relatively in a one-million-vocabulary isolated word recognition task.

Published in:

Automatic Speech Recognition and Understanding (ASRU), 2011 IEEE Workshop on

Date of Conference:

11-15 Dec. 2011