Robust sound classification through the representation of similarity using response fields derived from stimuli during early experience

Models of auditory processing, particularly of speech, face many difficulties. Included in these are variability among speakers, variability in speech rate, and robustness to moderate distortions such as time compression. We constructed a system based on ensembles of feature detectors derived from fragments of an onset-sensitive sound representation. This method is based on the idea of 'spectro-temporal response fields' and uses convolution to measure the degree of similarity through time between the feature detectors and the stimulus. The output from the ensemble was used to derive segmentation cues and patterns of response, which were used to train an artificial neural network (ANN) classifier. This allowed us to estimate a lower bound for the mutual information between the class of the input and the class of the output. Our results suggest that there is significant information in the output of our system, and that this is robust with respect to the exact choice of feature set, time compression in the stimulus, and speaker variation. In addition, the robustness to time compression in the stimulus has features in common with human psychophysics. Similar experiments using feature detectors derived from fragments of non-speech sounds performed less well. This result is interesting in the light of results showing aberrant cortical development in animals exposed to impoverished auditory environments during the developmental phase.