The HMM error model

The most popular model used in automatic speech recognition is the hidden Markov model (HMM). Though good performance has been obtained with such models there are well known limitations in its ability to model speech. For these reasons, a variety of modifications to the standard HMM topology have been proposed including factorial, or multi-stream, HMMs. This paper describes a new form of HMM based on transformation streams. A particular form of transformation stream is described, the HMM error model (HHM). This model may be viewed as a filter model, the transformation stream, and a residual model. The filter model transforms the original data into a space in which all the data is “similarly” distributed. This normalised data is then modelled using the residual model. The HEM is evaluated on a standard large vocabulary speaker independent speech recognition task, SwitchBoard. On this task significant reductions in word error rate are obtained over standard HMM-based systems.