Fast discriminative training for sequential observations with application to speaker identification

Qi Li, Biing-Hwang Juang
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).  
This paper presents a fast discriminative training algorithm for sequences of observations. It considers a sequence of feature vectors as one single composite token in training or testing. In contrast to the traditional EM algorithm, this algorithm is derived from a discriminative objective, aiming at directly minimizing the recognition error. Compared to the gradient-descent algorithms for discriminative training, this algorithm invokes a mild assumption which leads to closed-form formulas for
more » ... re-estimation, rather than relying on gradient search, without sacrificing the algorithmic rigor. As such, it is in general much faster than a descent based algorithm and does not need to determine the learning rate or step size. Our experiment shows that the proposed algorithm reduces error rate by 14.65, 66.46, and 100.00% for 1, 5, and 10 seconds of testing data respectively, in a speaker identification application.
doi:10.1109/icassp.2003.1202333 dblp:conf/icassp/LiJ03 fatcat:rdxa6nxpobew5fgde7krmgsauy