A task-independent filler modeling for robust key-phrase detection and verification is proposed. Instead of assuming task-specific lexical knowledge, our model is designed to characterize phrases depending on the speaking-style, thus can be trained with large corpora of different but similar tasks. We present two implementations of the portable and general model. The dialogue-style dependent model trained with the ATIS corpus is used as a filler and shown to be effective in detection-based speech understanding on different dialogue applications. The lecture-style dependent filler model trained with transcriptions of various oral presentations also improves the verification of key-phrases uttered during lectures.
Cite as: Kawahara, T., Ishizuka, K., Doshita, S., Lee, C.-H. (1998) Speaking-style dependent lexicalized filler model for key-phrase detection and verification. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 0761, doi: 10.21437/ICSLP.1998-819
@inproceedings{kawahara98c_icslp,
author={Tatsuya Kawahara and Kentaro Ishizuka and Shuji Doshita and Chin-Hui Lee},
title={{Speaking-style dependent lexicalized filler model for key-phrase detection and verification}},
year=1998,
booktitle={Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998)},
pages={paper 0761},
doi={10.21437/ICSLP.1998-819}
}