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Recurrent Neural Network Language Models (RNNLMs) have started to be used invarious fields of speech recognition due to their outstanding performance.However, the high computational complexity of RNNLMs has been a hurdle inapplying the RNNLM to a real-time Large Vocabulary Continuous SpeechRecognition (LVCSR). In order to accelerate the speed of RNNLM-based networksearches during decoding, we apply the General Purpose Graphic Processing Units(GPGPUs). This paper proposes a novel method of applying GPGPUs to RNNLM-basedgraph traversals. We have achieved our goal by reducing redundant computationson CPUs and amount of transfer between GPGPUs and CPUs. The proposed approachwas evaluated on both WSJ corpus and in-house data. Experiments shows that theproposed approach achieves the real-time speed in various circumstances whilemaintaining the Word Error Rate (WER) to be relatively 10% lower than that ofn-gram models.

Applying GPGPU to Recurrent Neural Network Language Model based Fast Network Search in the Real-Time LVCSR