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Speaker normalization and adaptation using second-order connectionist networks

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1 Author(s)
R. L. Watrous ; Siemens Corp. Res., Princeton, NJ, USA

A method for speaker normalization and adaption using connectionist networks is developed. A speaker-specific linear transformation of observations of the speech signal is computed using second-order network units. Classification is accomplished by a multilayer feedforward network that operates on the normalized speech data. The network is adapted for a new talker by modifying the transformation parameters while leaving the classifier fixed. This is accomplished by backpropagating classification error through the classifier to the second-order transformation units. This method was evaluated for the classification of ten vowels for 76 speakers using the first two formant values of the Peterson-Barney data. The results suggest that rapid speaker adaptation resulting in high classification accuracy can be accomplished by this method

Published in:

IEEE Transactions on Neural Networks  (Volume:4 ,  Issue: 1 )