TY - GEN
T1 - Speech utterance classification model training without manual transcriptions
AU - Wang, Ye-Yi
AU - Lee, John
AU - Acero, Alex
PY - 2006
Y1 - 2006
N2 - Speech utterance classification has been widely applied to a variety of spoken language understanding tasks, including call routing, dialog systems, and command and control. Most speech utterance classification systems adopt a data-driven statistical learning approach, which requires manually transcribed and annotated training data. In this paper we introduce a novel classification model training approach based on unsupervised language model adaptation. It only requires wave files of the training speech utterances and their corresponding classification destinations for modeling training. No manual transcription of the utterances is necessary. Experimental results show that this approach, which is much cheaper to implement, has achieved classification accuracy at the same level as the model trained with manual transcriptions. © 2006 IEEE.
AB - Speech utterance classification has been widely applied to a variety of spoken language understanding tasks, including call routing, dialog systems, and command and control. Most speech utterance classification systems adopt a data-driven statistical learning approach, which requires manually transcribed and annotated training data. In this paper we introduce a novel classification model training approach based on unsupervised language model adaptation. It only requires wave files of the training speech utterances and their corresponding classification destinations for modeling training. No manual transcription of the utterances is necessary. Experimental results show that this approach, which is much cheaper to implement, has achieved classification accuracy at the same level as the model trained with manual transcriptions. © 2006 IEEE.
UR - https://www.scopus.com/pages/publications/33947647975
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33947647975&origin=recordpage
U2 - 10.1109/ICASSP.2006.1660080
DO - 10.1109/ICASSP.2006.1660080
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1-4244-0469-X
VL - 1
SP - I 553-I 556
BT - 2006 IEEE International Conference on Acoustics, Speech and Signal Processing
PB - IEEE
T2 - 2006 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)
Y2 - 14 May 2006 through 19 May 2006
ER -