Acoustic Modelling for Large Vocabulary Continuous Speech Recognition

The presentation emphasises the need to carefully balance model complexity with available training data, and the methods of state-tying and mixture-splitting are described as examples of how this can be done.

Abstract

This chapter describes acoustic modelling in modern HMM-based LVCSR systems. The presentation emphasises the need to carefully balance model complexity with available training data, and the methods of state-tying and mixture-splitting are described as examples of how this can be done. Iterative parameter re-estimation using the forward-backward algorithm is then reviewed and the importance of the component occupation probabilities is emphasised. Using this as a basis, two powerful methods are presented for dealing with the inevitable mis-match between training and test data. Firstly, MLLR adaptation allows a set of HMM parameter transforms to be robustly estimated using small amounts of adaptation data. Secondly, MMI training based on lattices can be used to increase the inherent discrimination of the HMMs.