TY - GEN
T1 - Word topical mixture models for dynamic language model adaptation
AU - Chiu, Hsuan Sheng
AU - Chen, Berlin
PY - 2007
Y1 - 2007
N2 - This paper considers dynamic language model adaptation for Mandarin broadcast news recognition. A word topical mixture model (TMM) is proposed to explore the co-occurrence relationship between words, as well as the long-span latent topical information, for language model adaptation. The search history is modeled as a composite word TMM model for predicting the decoded word. The underlying characteristics and different kinds of model structures were extensively investigated, while the performance of word TMM was analyzed and verified by comparison with the conventional probabilistic latent semantic analysis-based language model (PLSALM) and trigger-based language model (TBLM) adaptation approaches. The large vocabulary continuous speech recognition (LVCSR) experiments were conducted on the Mandarin broadcast news collected in Taiwan. Very promising results in perplexity as well as character error rate reductions were initially obtained.
AB - This paper considers dynamic language model adaptation for Mandarin broadcast news recognition. A word topical mixture model (TMM) is proposed to explore the co-occurrence relationship between words, as well as the long-span latent topical information, for language model adaptation. The search history is modeled as a composite word TMM model for predicting the decoded word. The underlying characteristics and different kinds of model structures were extensively investigated, while the performance of word TMM was analyzed and verified by comparison with the conventional probabilistic latent semantic analysis-based language model (PLSALM) and trigger-based language model (TBLM) adaptation approaches. The large vocabulary continuous speech recognition (LVCSR) experiments were conducted on the Mandarin broadcast news collected in Taiwan. Very promising results in perplexity as well as character error rate reductions were initially obtained.
KW - Language model adaptation
KW - Probabilistic latent semantic analysis
KW - Speech recognition
KW - Trigger-based language model
KW - Word topical mixture model
UR - http://www.scopus.com/inward/record.url?scp=34547505668&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34547505668&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2007.367190
DO - 10.1109/ICASSP.2007.367190
M3 - Conference contribution
AN - SCOPUS:34547505668
SN - 1424407281
SN - 9781424407286
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - IV169-IV172
BT - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
T2 - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Y2 - 15 April 2007 through 20 April 2007
ER -