Accelerating hierarchical acoustic likelihood computation on graphics processors

The paper presents a method for performance improvements of a speech recognition system by moving a part of the computation acoustic likelihood computation onto a Graphics Processor Unit (GPU). In the system, GPU operates as a low cost powerful coprocessor for linear algebra operations. The paper compares GPU implementation of two techniques of acoustic likelihood computation: full Gaussian computation of all components and a significantly faster Gaussian selection method using hierarchical evaluation. The full Gaussian computation is an ideal candidate for GPU implementation because of its matrix multiplication nature. The hierarchical Gaussian computation is a technique commonly used on a CPU since it leads to much better performance by pruning the computation volume. Pruning techniques are generally much harder to implement on GPUs, nevertheless, the paper shows that hierarchical Gaussian computation can still be done using a GPU with much better performance than the full Gaussian computation.