In recent years there has been an upsurge in interest in approaches to speech pattern processing which go beyond the conventional hidden Markov model (HMM) framework. Current HMM-based models are fragile in noise, limited in their ability to handle pronunciation variation, and costly for large vocabulary spontaneous speech transcription. Their ability to represent dynamic behaviour is limited, and they 'are incompatible with modern, non-linear theories of phonology. This special issue of Computer Speech and Language on new computational paradigms for acoustic modeling in speech recognition brings together nine papers which are representative of current research in acoustic modeling which seeks, to overcome these limitations. (C) 2003 Elsevier Science Ltd. All rights reserved.