Distributed Listening in Automatic Speech Recognition

Abstract

While speech recognition systems have come a long way in the last forty years, there is still room for improvement. Although readily available, these systems are sometimes inaccurate and insufficient. The research presented here outlines a technique called Distributed Listening which demonstrates noticeable improvements to existing speech recognition methods. The Distributed Listening architecture introduces the idea of multiple, parallel, yet physically separate automatic speech recognizers called listeners. Distributed Listening also uses a piece of middleware, called an interpreter, which resolves multiple interpretations using a phrase resolution algorithm. The subsequent experiments of the research show that these efforts work together to increase the accuracy of the transcription of spoken utterances and Distributed Listening at worst, is as good as the best individual listener.