探索端到端语音识别的神经传感器

Eric Battenberg, Jitong Chen, Rewon Child, Adam Coates, Yashesh Gaur Yi Li, Hairong Liu, S. Satheesh, Anuroop Sriram, Zhenyao Zhu
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引用次数: 217

摘要

在这项工作中,我们对CTC、RNN-Transducer和基于注意力的Seq2Seq模型进行了端到端语音识别的实证比较。我们表明,在没有任何语言模型的情况下,Seq2Seq和RNN-Transducer模型在流行的Hub5'00基准测试中都优于带有语言模型的最佳CTC模型。在我们内部多样化的数据集上,这些趋势仍在继续——波束搜索后用语言模型重建的RNN-Transducer模型优于我们最好的CTC模型。这些结果简化了语音识别管道,因此解码现在可以纯粹地表示为神经网络操作。我们还研究了编码器结构的选择如何影响三种模型的性能——当所有编码器层仅向前时,以及当编码器积极地对输入表示进行下采样时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring neural transducers for end-to-end speech recognition
In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition. We show that, without any language model, Seq2Seq and RNN-Transducer models both outperform the best reported CTC models with a language model, on the popular Hub5'00 benchmark. On our internal diverse dataset, these trends continue — RNN-Transducer models rescored with a language model after beam search outperform our best CTC models. These results simplify the speech recognition pipeline so that decoding can now be expressed purely as neural network operations. We also study how the choice of encoder architecture affects the performance of the three models — when all encoder layers are forward only, and when encoders downsample the input representation aggressively.
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