Speech analysis and cognition using category-dependent features in a model of the central auditory system

It is well known that machines perform far worse than humans in recognizing speech and audio, especially in noisy environments. One method of addressing this issue of robustness is to study physiological models of the human auditory system and to adopt some of its characteristics in computers. As a first step in studying the potential benefits of an elaborate computational model of the primary auditory cortex (A1) in the central auditory system, we qualitatively and quantitatively validate the model under existing speech processing recognition methodology. Next, we develop new insights and ideas on how to interpret the model, and reveal some of the advantages of its dimension-expansion that may be potentially used to improve existing speech processing and recognition methods. This is done by statistically analyzing the neural responses to various classes of speech signals and forming empirical conjectures on how cognitive information is encoded in a category-dependent manner. We also establish a theoretical framework that shows how noise and signal can be separated in the dimension-expanded cortical space. Finally, we develop new feature selection and pattern recognition methods to exploit the category-dependent encoding of noise-robust cognitive information in the cortical response. Category-dependent features are proposed as features that "specialize" in discriminating specific sets of classes, and as a natural way of incorporating them into a Bayesian decision framework, we propose methods to construct hierarchical classifiers that perform decisions in a two-stage process. Phoneme classification tasks using the TIMIT speech database are performed to quantitatively validate all developments in this work, and the results encourage future work in exploiting high-dimensional data with category(or class)-dependent features for improved classification or detection.