Recent success of the Tacotron speech synthesis architecture and its variants
in producing natural sounding multi-speaker synthesized speech has raised the
exciting possibility of replacing expensive, manually transcribed,
domain-specific, human speech that is used to train speech recognizers. The
multi-speaker speech synthesis architecture can learn latent embedding spaces
of prosody, speaker and style variations derived from input acoustic
representations thereby allowing for manipulation of the synthesized speech. In
this paper, we evaluate the feasibility of enhancing speech recognition
performance using speech synthesis using two corpora from different domains. We
explore algorithms to provide the necessary acoustic and lexical diversity
needed for robust speech recognition. Finally, we demonstrate the feasibility
of this approach as a data augmentation strategy for domain-transfer.
We find that improvements to speech recognition performance is achievable by
augmenting training data with synthesized material. However, there remains a
substantial gap in performance between recognizers trained on human speech
those trained on synthesized speech.

本文探讨使用不同领域的双语料库进行音频识别训练的数据增强方法，通过使用 Tacotron 合成语音的方式，来提升语音识别的性能。研究表明，使用合成材料进行数据增强可以实现对语音识别性能的提高，但是自然语音和合成语音训练识别器的性能仍有很大的差距。