Integration of Stochastic and Dynamical Methods for Speech Technology

Marquette University, Milwaukee WI

Investigators

Abstract

This project focuses on the creation of a stochastic representation for the phase-space embeddings of dynamical systems, for application to the task of speech classification and recognition. The research team will develop a general stochastic model for such signal embeddings, test the model through classification simulations, then apply the technique to both isolated and continuous speech recognition. The goal of the research is to discover time-domain analysis techniques using dynamical systems methods that will lead to improved analysis of speech signals and to improvements in speech recognition accuracy. This approach represents the integration of two traditionally distinct research fields: statistical signal processing and chaotic systems. Since signal processing is fundamentally based on linear systems theory and the study of chaos is inherently non-linear, these fields have little or no overlap outside of the fact that both attempt to model the behavior of physical systems. This research integrates these very different viewpoints by applying stochastic analysis and modeling tools from the signal processing field to the problem of analyzing embedded phase spaces obtained from chaotic systems analysis of time-series signals. The results will lead to a significant gain in our fundamental understanding of the characteristics and analysis of speech signals, with potential long-term application to other areas of speech processing such as speech coding and synthesis. The impact of developing these new technologies and applying them to the speech recognition task extends into both the machine learning and signal processing communities; specifically, the development of time-domain characterization methods is directly applicable to many problems of interest in the chaos and non-linear modeling domain, and will demonstrate an ability to concretely measure differences between the phase-space attractors of chaotic systems.