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Abstract
Nature’s sensory systems, their corresponding processing modules, and, in some cases (such as speech) also the structure of message-carrying sensory data, have all co-evolved to ensure survival of their respective species, and hence reached a high level of effectiveness. Therefore, we argue that human-like processing often represents the most effective engineering processing for sensory data. However, we also argue that such human-like processing does not (and perhaps should not) be derived by indiscriminate emulation of all mechanisms and properties of biological systems. Rather, we think that our designs should selectively apply key human-like concepts that address the particular weaknesses of artificial algorithms, and that have not yet fully evolved in the course of the historical evolution of speech technology. We also show that these concepts may sometimes directly emerge in the course of optimizing performance of the machine algorithms on target data. The approach will be illustrated on a several specific examples of algorithms that are currently being successfully used in main stream applications.

Biography
Hynek Hermansky is a Director of Research at the IDIAP Research Institute Martigny and a Professor at the Swiss Federal Institute of Technology at Lausanne, Switzerland (among a number of other mostly unpaid affiliations). He has been working in speech processing for over 30 years, previously as a Research Fellow at the University of Tokyo, a Research Engineer at Panasonic Technologies in Santa Barbara, California, a Senior Member of Research Staff at U S WEST Advanced Technologies, and a Professor and Director of the Center for Information Processing at the OGI School of the Oregon Health and Sciences University, Portland, Oregon.