Developing Automatic Speech Recognition (ASR) for low-resource languages is a challenge due to the small amount of transcribed audio data. For many such languages, audio and text are available separately, but not audio with transcriptions. Using text, speech can be synthetically produced via text-to-speech (TTS) systems. However, many low-resource languages do not have quality TTS systems either. We propose an alternative: produce synthetic audio by running text from the target language through a trained TTS system for a higher-resource pivot language. We investigate when and how this technique is most effective in low-resource settings. In our experiments, using several thousand synthetic TTS text-speech pairs and duplicating authentic data to balance yields optimal results. Our findings suggest that searching over a set of candidate pivot languages can lead to marginal improvements and that, surprisingly, ASR performance can by harmed by increases in measured TTS quality. Application of these findings improves ASR by 64.5\% and 45.0\% character error reduction rate (CERR) respectively for two low-resource languages: Guaran\'i and Suba.

研究聚焦在如何利用高资源语言的已训练 TTS 系统，使其产生的合成语音能提高低资源语言的 ASR 性能。结果表明，使用数千个 TTS 合成文本-语音对并通过平衡真实数据可得到最佳结果，但过高的 TTS 质量可能会影响 ASR 性能。在瓜拉尼语和苏巴语等两种低资源语言上，应用这些发现可将 ASR 性能提高 64.5％和字符误差减少率 (CERR) 45.0％。

TTS增强通过旋转语言何时有用？