Abstract

This paper proposes an unsupervised, batch-type, class-based language model adaptation method for spontaneous speech recognition. The word classes are automatically determined by maximizing the average mutual information between the classes using a training set. A class-based language model is built based on recognition hypotheses obtained using a general word-based language model, and linearly interpolated with the general language model. All the input utterances are re-recognized using the adapted language model. The proposed method was applied to the recognition of spontaneous presentations and was found to be effective in improving the recognition accuracy for all the presentations. The best condition was found to be using 100 word classes, and in this condition 2.3% of the absolute value improvement in the word accuracy averaged over all the speakers was achieved.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.