论文标题
将GPGPU应用于实时LVCSR中的基于复发性神经网络语言模型的快速网络搜索
Applying GPGPU to Recurrent Neural Network Language Model based Fast Network Search in the Real-Time LVCSR
论文作者
论文摘要
复发性的神经网络语言模型（RNNLMS）由于表现出色而开始在各个语音识别领域中使用。但是，RNNLMS的高计算复杂性是将RNNLM应用于实时大型词汇连续语音识别（LVCSR）的障碍。为了在解码过程中加速基于RNNLM的网络搜索的速度，我们应用了通用图形处理单元（GPGPU）。本文提出了一种将GPGPU应用于基于RNNLM的图形遍历的新方法。我们通过减少对CPU的冗余计算以及GPGPU和CPU之间的转移量来实现我们的目标。在WSJ语料库和内部数据上都评估了所提出的方法。实验表明，所提出的方法在各种情况下实现了实时速度，同时保持单词错误率（WER）比N-Gram模型低10％。
Recurrent Neural Network Language Models (RNNLMs) have started to be used in various fields of speech recognition due to their outstanding performance. However, the high computational complexity of RNNLMs has been a hurdle in applying the RNNLM to a real-time Large Vocabulary Continuous Speech Recognition (LVCSR). In order to accelerate the speed of RNNLM-based network searches during decoding, we apply the General Purpose Graphic Processing Units (GPGPUs). This paper proposes a novel method of applying GPGPUs to RNNLM-based graph traversals. We have achieved our goal by reducing redundant computations on CPUs and amount of transfer between GPGPUs and CPUs. The proposed approach was evaluated on both WSJ corpus and in-house data. Experiments shows that the proposed approach achieves the real-time speed in various circumstances while maintaining the Word Error Rate (WER) to be relatively 10% lower than that of n-gram models.
