Neural Circuits for Any-Time Phrase Recognition with Applications in Cognitive Models and Human-Robot Interaction

Abstract

Humans are remarkably good at recognizing spoken language, even in very noisy environments. Yet, artificial speech rec-ognizers do not reach human level performance, nor do they typically even attempt to model human speech processing. In this paper, we introduce a biologically plausible neural model of real-time spoken phrase recognition which shows how the time-varying spiking activity of neurons can be integrated into word tokens. We present a proof-of-concept implementation of the model, which shows promise both in terms of recog-nition accuracy as well as recognition speed. The model is also pragmatically useful to cognitive modelers who require robust any-time speech recognition for their models such as real-time models of human-robot interaction. We thus also present such an example of embedding our model in a larger cognitive model, along with offline analysis of its performance on a speech corpus.