JOURNAL ARTICLE

The RWTH large vocabulary continuous speech recognition system

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.

Keywords:
Computer science Speech recognition Hidden Markov model Vocabulary Pronunciation Lexicon Normalization (sociology) Beam search Artificial intelligence Linear discriminant analysis Word (group theory) Natural language processing Search algorithm Linguistics Algorithm

Metrics

46
Cited By
8.54
FWCI (Field Weighted Citation Impact)
21
Refs
0.98
Citation Normalized Percentile
Is in top 1%
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Citation History

Topics

Speech Recognition and Synthesis
Physical Sciences →  Computer Science →  Artificial Intelligence
Speech and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing
Advanced Data Compression Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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