Large scale MMIE training for conversational telephone speech recognition
PC Woodland, Daniel Povey · 2000
This paper describes a lattice-based framework for maximum mutual information estimation (MMIE) of HMM parameters which has been used to train HMM systems for conversational telephone speech transcription using up to 265 hours of training data. These experiments represent the largest-scale application of discriminative training techniques for speech recognition of which the authors are aware, and have led to significant reductions in word error rate for both triphone and quinphone HMMs compared to our best models trained using maximum likelihood estimation. The use of MMIE training was a key contributer to the performance of the CU-HTK March 2000 Hub5 evaluation system. 1 INTRODUCTION The model parameters in HMM based speech recognition systems are normally estimated using Maximum Likelihood Estimation (MLE). If certain conditions hold, including model correctness, then MLE can be shown to be optimal. However, when estimating the parameters of HMM-based speech recognisers, the true d...