DARPA resource management benchmark test results June 1990
HLT '90 Proceedings of the workshop on Speech and Natural Language
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
On speaker-independent, speaker-dependent, and speaker-adaptive speech recognition
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
| Hi-index | 0.00 |
A successful speaker normalization mechanism will not only be useful to speaker adaptation but also speaker-independent speech recognition. In this paper, a codeword-dependent neural network (CDNN) is presented for the study of speaker adaptation. The CDNN is used as a nonlinear mapping function to transform speech data between two speakers. The mapping function is characterized by a number of important properties. First, the assembly of mapping functions enhances overall mapping quality. Second, multiple input vectors are used simultaneously in the transformation. This not only makes full use of dynamic information but also alleviates possible errors in the supervision data. Finally, the mapping function is derived from training data and the quality will dependent on the available amount of training data. Based on speaker-dependent models, performance evaluation showed that speaker normalization significantly reduced the error rate from 41.9% to 5.0%.