We present a method to perform first-pass large vocabulary continuous speech
recognition using only a neural network and language model. Deep neural network
acoustic models are now commonplace in HMM-based speech recognition systems,
but building such systems is a complex, domain-specific task. Recent work
demonstrated the feasibility of discarding the HMM sequence modeling framework
by directly predicting transcript text from audio. This paper extends this
approach in two ways. First, we demonstrate that a straightforward recurrent
neural network architecture can achieve a high level of accuracy. Second, we
propose and evaluate a modified prefix-search decoding algorithm. This approach
to decoding enables first-pass speech recognition with a language model,
completely unaided by the cumbersome infrastructure of HMM-based systems.
Experiments on the Wall Street Journal corpus demonstrate fairly competitive
word error rates, and the importance of bi-directional network recurrence.

本文提出了使用神经网络和语言模型进行大词汇连续语音识别的方法，并通过一种改进的前缀搜索解码算法，使得该方法完全不依赖于基于 HMM 的架构，实现了完全自主的一遍语音识别。在对华尔街日报语料库的实验中，取得了较为具有竞争力的错误率，并且证明了双向网络的重要性。