ITR-NHS-DMC: Making Speech Recognition Pervasive by Migrating it Into Silicon

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

NSF ITR Proposal 0426904 Making Speech Recognition Pervasive by Migrating it Into Silicon Rob A. Rutenbar (CMU), Tsuhan Chen (CMU), Robert W. Brodersen (U.C. Berkeley) ABSTRACT Whether running on a single cell phone or a conventional PC - all of today's state-of-the-art speech recognizers exist as complex software running on conventional computers. This is profoundly limiting for national and homeland security applications in which mobility or stealth are essential. Today's state-of-the-art speech recognizers fully occupy the resources of a modern desktop PC; but we cannot deploy such hardware in scenarios where size, covertness, long-life, and untethered operation are essential. Our cell phones last a week if we do not use them; they last a few hours when we actually speak to them. To remedy this, we must move the core of today's most successful speech recognition strategies directly into silicon. We propose to design a silicon speech recognition architecture that can offer at least 100 times better energy efficiency than today's software solutions. We will explore performance trade-offs by extending field programmable gate array (FPGA) emulation technology, so that proposed chip designs may be rapidly evaluated executing real-world problems involving hours of voice data. The Carnegie Mellon / Berkeley team brings decades of experience with silicon design, low-power design, and speech recognition to this effort. The goal of the project is to liberate speech recognition from the artificial constraints of its current software-only form, and to make it a reliable, pervasive technology for the field-oriented national and homeland security applications where it is today unsuitable.