ACCELERATING LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION ON HETEROGENEOUS CPU-GPU PLATFORMS

While prior works have demonstrated the effectiveness of Graphic-Processing Units (GPUs) for limited vocabulary speech recognition, these methods were unsuitable for recognition with large language models. To overcome this limitation, previously we introduced a novel "on-the-fly rescoring" approach in which search was performed over a WFST-network composed with a unigram language model on the GPU, and partial hypotheses were rescored on-the-fly using a large language model stored on the CPU. In this paper, we extend our previous algorithm to enable on-the-fly rescoring to be performed over an H-level network composed with any n-gram language model, and show that using a longer language model history in the H-level network improves decoding speed. We demonstrate that large language models can be applied on-the-fly with no degradation in decoding speed, realizing a LVCSR system that performs recognition over 22x faster than a CPU implementation with no loss in recognition accuracy.