Class-based n-gram models of natural language
Computational Linguistics
Towards increasing speech recognition error rates
Speech Communication
MMIE training of large vocabulary recognition systems
Speech Communication
A unifying review of linear Gaussian models
Neural Computation
Stochastic pronunciation modelling from hand-labelled phonetic corpora
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Tree-based state tying for high accuracy acoustic modelling
HLT '94 Proceedings of the workshop on Human Language Technology
A one pass decoder design for large vocabulary recognition
HLT '94 Proceedings of the workshop on Human Language Technology
An investigation of PLP and IMELDA acoustic representations and of their potential for combination
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Probabilistic classification of HMM states for large vocabulary continuous speech recognition
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
The 1998 HTK system for transcription of conversational telephone speech
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
SWITCHBOARD: telephone speech corpus for research and development
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
A novel template matching approach to speaker-independent arabic spoken digit recognition
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
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Automatic continuous speech recognition (CSR) is sufficiently mature that a variety of real world applications are now possible including large vocabulary transcription and interactive spoken dialogues. This paper reviews the evolution of the statistical modelling techniques which underlie current-day systems, specifically hidden Markov models (HMMs) and N-grams. Starting from a description of the speech signal and its parameterisation, the various modelling assumptions and their consequences are discussed. It then describes various techniques by which the effects of these assumptions can be mitigated. Despite the progress that has been made, ther limitations of current modelling techniques are still evident. The paper therefore concludes with a brief review of some of the more fundamental modelling work now in progress.