Connectionist Speaker Normalization with Generalized Resource Allocating Networks
Cesare Furlanello, Diego Giuliani, Edmondo Trentin, Istituto Per La Ricerca, Istituto Per La Ricerca, Istituto Per La Ricerca · 1995
The paper presents a rapid speaker-normalization technique based on neural network spectral mapping. The neural network is used as a front-end of a continuous speech recognition system (speakerdependent, HMM-based) to normalize the input acoustic data from a new speaker. The spectral difference between speakers can be reduced using a limited amount of new acoustic data (40 phonetically rich sentences). Recognition error of phone units from the acoustic-phonetic continuous speech corpus APASCI is decreased with an adaptability ratio of 25%. We used local basis networks of elliptical Gaussian kernels, with recursive allocation of units and on-line optimization of parameters (GRAN model). For this application, the model included a linear term. The results compare favorably with multivariate linear mapping based on constrained orthonormal transformations. 1 INTRODUCTION Speaker normalization methods are designed to minimize inter-speaker variations, one of the principal error sources in a...