Implementing a high accuracy speaker-independent continuous speech recognizer on a fixed-point DSP

Abstract

Continuous speech recognition is a resource-intensive algorithm. Commercial dictation software requires more than 10 Mbytes to install on the disk and 32 Mbytes RAM to run the application. A typical embedded system can not afford this much RAM because of its high cost and power consumption; it also lacks disk to store the large amount of static data (e.g. acoustic models). We have been working on optimization of a small vocabulary speech recognizer suitable for implementation on a 16-bit fixed-point DSP. This recognizer supports sophisticated continuous density, tied-mixtures Gaussians, parallel model combination, and a noise-robust utterance detection algorithm. The fixed-point version achieves the same performance as the floating-point version. The algorithm runs real-time on a 100 MHz, 16-bit, fixed-point Texas Instruments TMS320C5410 even for the most challenging continuous digit dialing with hands-free microphone in driving conditions.