Computer Science > Computation and Language
[Submitted on 13 Sep 2019 (v1), last revised 28 Sep 2019 (this version, v2)]
Title:A Comparative Study on Transformer vs RNN in Speech Applications
View PDFAbstract:Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS). This paper focuses on an emergent sequence-to-sequence model called Transformer, which achieves state-of-the-art performance in neural machine translation and other natural language processing applications. We undertook intensive studies in which we experimentally compared and analyzed Transformer and conventional recurrent neural networks (RNN) in a total of 15 ASR, one multilingual ASR, one ST, and two TTS benchmarks. Our experiments revealed various training tips and significant performance benefits obtained with Transformer for each task including the surprising superiority of Transformer in 13/15 ASR benchmarks in comparison with RNN. We are preparing to release Kaldi-style reproducible recipes using open source and publicly available datasets for all the ASR, ST, and TTS tasks for the community to succeed our exciting outcomes.
Submission history
From: Shigeki Karita [view email][v1] Fri, 13 Sep 2019 16:27:08 UTC (883 KB)
[v2] Sat, 28 Sep 2019 11:11:38 UTC (889 KB)
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