Fast acoustic computations using graphics processors
Paul R. Dixon, Tasuku Oonishi, Sadaoki Furui · 2009
In this paper we present a fast method for computing acoustic likelihoods that makes use of a graphics processing unit (GPU). After enabling the GPU acceleration the main processor runtime dedicated to acoustic scoring tasks is reduced from the largest consumer to just a few percent even when using mixture models with a large number of Gaussian components. The results show a large reduction in decoding time with no change in accuracy and we also show by using a 16bit half precision floating point format for the acoustic model parameters, storage requirements can be halved with no reduction in accuracy.