The role of attention is growing in importance as speech recognition moves into more challenging environments. Attention is not a factor when we speak into a close-talking microphone with a push-to-talk button. But the acoustic world in which we live is not so simple; multiple sources add together in a confusing mixture of sound, making it difficult to analyze any one source. In such settings, attention serves two important purposes: 1) It helps us to focus our computational resources on the problem at hand, and 2) it helps us to piece together portions of our environment to solve a single task. Understanding how attention works may be the critical advance that allows us to build speech-recognition machines that cope with complex, everyday settings robustly and flexibly, just as a human listener does.

I’ve had the privilege to help lead three recent projects on attention at the Neuromorphic Cognition Workshop [2]. These projects have studied different parts of attention in a short, focused, working workshop. During the summer of 2011 we studied and built a complete cocktail-party system, with both top-down and bottom-up signals. In the summer of 2012 we used EEG signals to “listen” to a subject’s brain and decode which of two speech signals he was attending. I will describe the Neuromorphic Engineering Workshop, and then the two latest attention projects.