New strategies for improving speech enhancement.

We describe two design strategies that could substantially improve the performance of speech enhancement systems. Results from a preliminary study of pulse recovery are presented to illustrate the potential benefits of such strategies. The first strategy is a direct application of a non-linear, adaptive signal processing approach for recovery of speech in noise. The second strategy optimizes performance by maximizing the enhancement system's ability to evoke target speech percepts. This approach may lead to better performance because the design is optimized on a measure directly related to the ultimate goal of speech enhancement: accurate communication of the speech percept. In both systems, recently developed 'neural network' learning algorithms can be used to determine appropriate parameters for enhancement processing.

Computational neural networks: enhancing supervised learning algorithms via self-organization.

A neural network processing scheme is proposed which utilizes a self-organizing Kohonen feature map as the front end to a feedforward classifier network. The results of a series of benchmarking studies based upon artificial statistical pattern recognition tasks indicate that the proposed architecture performs significantly better than conventional feedforward classifier networks when the decision regions are disjoint. This is attributed to the fact that the self-organization process allows internal units in the succeeding classifier network to be sensitive to a specific set of features in the input space at the outset of training.

Classification of somatosensory-evoked potentials recorded from patients with severe head injuries.

The problem of sensory evoked potential (EP) assessment in the critical care setting to isolate damage in specific neuronal pathways, as manifested by certain abnormalities in the response waveform is being addressed using neural networks. An existing visually based grading scheme (GGS, for Greenberg grading system) for somatosensory EPs collected from patients with severe head injuries is being automated. The collection of data used in this research, which consist of somatosensory-evoked potential (SEP) waveforms collected from patients with head injuries is described. The way the system (called Pathfinder) works is described, and results obtained with it are presented.