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ICASSP
2009
IEEE

Training and adapting MLP features for Arabic speech recognition

10 years 4 months ago
Training and adapting MLP features for Arabic speech recognition
Features derived from Multi-Layer Perceptrons (MLPs) are becoming increasingly popular for speech recognition. This paper describes various schemes for applying these features to state-of-the-art Arabic speech recognition: the use of MLP-features for short-vowel modelling in graphemic systems; rapid discriminative model training by standard PLP feature lattice re-use; and MLP feature adaptation using Linear Input Networks (LIN). The use of rapid training using MLP features and their use for short-vowel modelling and LIN adaptation gave reductions in word error rate. However significant improvements over explicit short-vowel modelling with standard multi-pass adaptation were not obtained, although they were useful in combination.
J. Park, Frank Diehl, M. J. F. Gales, Marcus Tomal
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors J. Park, Frank Diehl, M. J. F. Gales, Marcus Tomalin, Philip C. Woodland
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