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  Acoustic Front-End Optimization for Large Vocabulary Speech Recognition (1997) [11 citations — 8 self]

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by L. Welling, N. Haberl, H. Ney
Proc. EUROSPEECH
http://www.informatik.rwth-aachen.de/I6/PostScript/InterneArbeiten/Welling_FrontEnd_EUROSPEECH97_18Dez98.ps
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Abstract:

In this paper we describe experiments with the acoustic front--end of our large vocabulary speech recognition system. In particular, two aspects are studied: 1) linear transforms for feature extraction and 2) the modelling of the emission probabilities. Experiments are reported on a 5000--word task of the ARPA Wall Street Journal database. For the linear transforms our main results are: ffl Filter--bank coefficients yield a word error rate of

Citations

3033 Pattern Classification and Scene Analysis – Duda, Hart - 1973
929 Biing-Hwang Juang, Fundamentals of Speech Recognition – Rabiner - 1993
363 Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences – Davis, Mermelstein - 1980
35 A Comparison of Several Acoustic Representations for Speech Recognition with Degraded and Undegraded Speech," [58] [59 – Hunt, Lefebvre - 1989
30 Large vocabulary continuous speech recognition using word graphs – Aubert, Ney
17 Improvements in connected digit recognition using linear discriminant analysis and mixture densities – Haeb-Umbach, Geller, et al. - 1994
16 Acoustic Modeling of Phoneme Units for Continuous Speech Recognition – Ney - 1990
16 HTK: Hidden Markov Model Toolkit V1.4 – Young - 1993
11 Connected Digit Recognition using Statistical Template Matching – Welling, Ney, et al. - 1995
10 A Comparative Study of Linear Feature Transformation Techniques for Automatic Speech Recognition – Eisele, Haeb-Umbach, et al. - 1996
8 Experiments with linear feature extraction in speech recognition – Beulen, Welling, et al. - 1995
1 Ney "State tying for context dependent phoneme models – Beulen, Branch, et al. - 1997
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