Acceleration of HMM-based speech recognition system by parallel FPGA Gaussian calculation

Abstract

An FPGA-based custom core which computes the Gaussian calculation portion of a Hidden Markov Model (HMM) based speech recognition system, is presented. The work is part of the development of a custom embedded system which will provide speaker independend, large vocabulary continuos speech recognition and is currently presented as a hardware/software codesign. By de-coupling the Gaussian calculation from the backend search, calculation of Gaussian results is performed with minimal communication between backend search software and an FPGA based Gaussian core. Several implementations have been investigated in order to minimize memory bandwidth and FPGA resource requirements and are presented. The system has been implemented using an Alpha Data XCR-5T1, reconfigurable computer housing a Virtex 5 SX95T FPGA and has achieved better than real-time performance at 133MHz. The core has been tested and is capable of calculating a full set of Gaussian results from 3825 acoustic models in 5.3ms which coupled with a backend search of 5000 words has provided over 80% accuracy.