An investigation into front-end signal processing for speaker normalization

Our investigation into the front-end signal processing for maximum likelihood based speaker normalization reveals that, in the linear scaling model, it is more appropriate (and evidently more correct) to assume that the spectral envelopes of any two speakers for the same sound are linearly scaled versions of one another, rather than assuming that the magnitudes of the whole spectra (including pitch harmonics) are scaled. The use of the proposed model and its implementation results in about 4% and 7% relative improvement for adults and children respectively on a digit recognition task.