Flexible Low Power Probability Density Estimation Unit For Speech Recognition

Abstract

This paper describes the hardware architecture for a flexible probability density estimation unit to be used in a large vocabulary speech recognition system, and targeted for mobile platforms. The speech recognition system is based on hidden Markov models and consists of two computationally intensive parts - the probability density estimation using Gaussian distributions, and the Viterbi decoding. The power hungry nature of these computations prevents porting the application successfully to mobile devices. We have designed a flexible probability estimation unit that is both power efficient and meets real time requirements while being flexible enough to handle emerging speech recognition techniques. The flexible nature of the design allows it to utilize emerging power and computation reduction techniques (at the algorithm level) to achieve up to an 80% power reduction as compared to conventional designs