Abstract

In this paper we describe an automatic prosody labeling framework that exploits both language and speech information intended for the purpose of incorporating prosody within a speech-to-speech translation framework. We propose a maximum entropy syntactic- prosodic model that achieves an accuracy of 85.22% and 91.54% for pitch accent and boundary tone labeling on the Boston University Radio News corpus. We model the acoustic-prosodic stream with two different models, one a maximum entropy model and the other a traditional HMM. We finally couple the syntactic-prosodic and acoustic-prosodic components to achieve a pitch accent and boundary tone classification accuracy of 86.01% and 93.09% respectively.

Date

December 10, 2006

Authors

Vivek Rangarajan, Shrikanth Narayanan, Srinivas Bangalore

Conference

2006 IEEE Spoken Language Technology Workshop

Pages

74-77

Publisher

IEEE