In real-time speech recognition applications, the latency is an important
issue. We have developed a character-level incremental speech recognition (ISR)
system that responds quickly even during the speech, where the hypotheses are
gradually improved while the speaking proceeds. The algorithm employs a
speech-to-character unidirectional recurrent neural network (RNN), which is
end-to-end trained with connectionist temporal classification (CTC), and an
RNN-based character-level language model (LM). The output values of the
CTC-trained RNN are character-level probabilities, which are processed by beam
search decoding. The RNN LM augments the decoding by providing long-term
dependency information. We propose tree-based online beam search with
additional depth-pruning, which enables the system to process infinitely long
input speech with low latency. This system not only responds quickly on speech
but also can dictate out-of-vocabulary (OOV) words according to pronunciation.
The proposed model achieves the word error rate (WER) of 8.90% on the Wall
Street Journal (WSJ) Nov'92 20K evaluation set when trained on the WSJ SI-284
training set.

本文提出了一种基于 RNN 的字符级增量语音识别系统，使用 CTC 进行端到端训练，在短时间内响应语音输入，通过基于树的在线 Beam Search 算法实现字级别的识别，该系统不仅能够响应不断输入的语音，还能根据发音来发音对语音的字词进行识别。在训练数据集上，该模型将字错率降低至 8.90％。