Title :
The RWTH speech recognition system and spoken document retrieval
Author :
Ney, H. ; Welling, L. ; Ortmanns, S. ; Beulen, K. ; Wessel, F.
Author_Institution :
Lehrstuhl fur Inf., Tech. Hochschule Aachen, Germany
fDate :
31 Aug-4 Sep 1998
Abstract :
We present an overview of the RWTH Aachen large vocabulary continuous speech recognizer. The recognizer is based on continuous density hidden Markov models and a time-synchronous left-to-right beam search strategy. Experimental results on the ARPA Wall Street Journal (WSJ) corpus verify the effects of several system components, namely linear discriminant analysis, vocal tract normalization, pronunciation lexicon and cross-word triphones, on the recognition performance. Finally, the extension of the recognition system towards spoken document retrieval is discussed
Keywords :
hidden Markov models; information retrieval; search engines; speech recognition; ARPA Wall Street Journal corpus; RWTH Aachen large vocabulary continuous speech recognizer; RWTH speech recognition system; continuous density hidden Markov models; cross-word triphones; linear discriminant analysis; pronunciation lexicon; recognition performance; spoken document retrieval; time-synchronous left-to-right beam search; vocal tract normalization; Acoustic beams; Cepstrum; Filters; Hidden Markov models; Linear discriminant analysis; Linear regression; Mel frequency cepstral coefficient; Speech recognition; Vectors; Vocabulary;
Conference_Titel :
Industrial Electronics Society, 1998. IECON '98. Proceedings of the 24th Annual Conference of the IEEE
Conference_Location :
Aachen
Print_ISBN :
0-7803-4503-7
DOI :
10.1109/IECON.1998.724029
