This Tuesday, Adam Stiff will be talking about his efforts to take a dynamical systems-based approach to speech recognition (yes, via spiking networks):
Speech can be viewed as a dynamical system (i.e. a continuous function from a state space onto itself, with state changing continuously through time), and in very broad terms, this perspective should be fairly uncontroversial (indeed, it is often the basis for models of speech production). It is, however, extremely impractical, due to the huge number of nonlinear variables involved, and the apparent lack of a framework for learning them. Thus, the tools developed by mathematicians to understand nonlinear dynamical systems have not been widely utilized in attempts at automated speech recognition. I’ll argue that the brain does employ such techniques, and that adapting them could produce benefits in terms of energy efficiency, scalability, and robustness to the problem of catastrophic forgetting in the face of ongoing learning. Furthermore, observation of “fast” (sub-millisecond) dynamics may theoretically offer some benefits for recognition accuracy, and act as a bottom-up factor in learning phone segmentation. I also hope to exhibit some results from an (ongoing) phone classification experiment, to identify constraints that should be respected by a successful implementation of some of these ideas.