Channel-Dependent Compensation for Noisy Acoustic Data

One on the major problems in channel compensation for acoustic data collected over the telephone line arises from the wide variety of involved channels (whenever no fixed line is used). Attempts to perform channel compensation by way of a feature mapping between noisy and clean acoustic spaces as whole resulted unsatisfactory. This lead to the idea of channel-dependent transformations, i.e. channel typologies (classes) are looked for, and feature mappings are accomplished differently according to individual classes. This work discusses experimental results obtained with a connectionist architecture, based on Simple Linear Perceptrons, to approach the problem of channel-dependent compensation. The well-known k-means clustering algorithm has been used to partition background-noise of signals into a codebook of channels prototypes. Preliminary recognition experiments are reported, showing some properties of the proposed approach