Sixth International Conference on Spoken Language Processing
October 16-20, 2000
A Novel Loss Function for the Overall Risk Criterion Based Discriminative Training of HMM Models
Janez Kaiser, Bogomir Horvat, Zdravko Kacic
University of Maribor, Faculty of Electrical Engineering and Computer Science,
In this paper, we propose a novel loss function for the
overall risk criterion estimation of hidden Markov models.
For continuous speech recognition, the overall risk criterion
estimation with the proposed loss function aims to
directly maximise word recognition accuracy on the training
database. We propose reestimation equations for the
HMM parameters, which are derived using the Extended
Baum-Welch algorithm. Using HMM, trained with the
proposed method, a decrease of word recognition error rate
of up to 17.3% has been achieved for the phoneme
recognition task on the TIMIT database.
Kaiser, Janez / Horvat, Bogomir / Kacic, Zdravko (2000):
"A novel loss function for the overall risk criterion based discriminative training of HMM models",
In ICSLP-2000, vol.2, 887-890.