Title :
Transcription of broadcast news-some recent improvements to IBM´s LVCSR system
Author :
Polymenakos, L. ; Olsen, P. ; Kanvesky, D. ; Gopinath, R.A. ; Gopalakrishnan, P.S. ; Chen, S.
Author_Institution :
Dept. of Comput. Sci., IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA
Abstract :
This paper describes extensions and improvements to IBM´s large vocabulary continuous speech recognition (LVCSR) system for transcription of broadcast news. The recognizer uses an additional 35 hours of training data over the one used in the 1996 Hub4 evaluation. It includes a number of new features: optimal feature space for acoustic modeling (in training and/or testing), filler-word modeling, Bayesian information criterion (BIC) based segment clustering, an improved implementation of iterative MLLR and 4-gram language models. Results using the 1996 DARPA Hub4 evaluation data set are presented
Keywords :
Bayes methods; acoustic signal processing; broadcasting; grammars; information theory; speech recognition; speech synthesis; 4-gram language models; Bayesian information criterion; DARPA Hub4 evaluation data set; IBM LVCSR system; acoustic modeling; broadcast news transcription; filler-word modeling; iterative MLLR; large vocabulary continuous speech recognition; optimal feature space; segment clustering; testing; training; training data; Acoustic testing; Bandwidth; Broadcasting; Maximum likelihood linear regression; Speech analysis; Speech enhancement; Speech recognition; Telephony; Training data; Vocabulary;
Conference_Titel :
Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-4428-6
DOI :
10.1109/ICASSP.1998.675411
