Speaker normalization using second‐order connectionist networks

A method for speaker‐adaptive classification of vowels using connectionist networks is developed. A speaker‐specific linear transformation of observations of the speech signal is computed using second‐order connectionist network units. Vowel classification is accomplished by a multilayer network, which operates on the transformed speech data. The network may be adapted for a new talker by modifying the transformation parameters while leaving the classifier network fixed. This is accomplished by back‐propagating classification error through the classifier to the transformation parameters. A model of this type was evaluated for 75 speakers using the first two formant values of the Peterson/Barney data (ten vowels). When the speaker‐dependent transformation and nonlinear classifier were simultaneously optimized, a vowel recognition accuracy of 96.8% was obtained. However, when adapted from average speaker parameters, the network was shown to be very sensitive to initial values. When the linear transformation...