Training and adapting MLP features for Arabic speech recognition
J. Park, Frank Diehl, Mark Gales, Marcus Tomalin, Philip C. Woodland · 2009
Features derived from multilayer 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 reuse; 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.