In recent years, Text-To-Speech (TTS) has been used as a data augmentation technique for speech recognition to help complement inadequacies in the training data. Correspondingly, we investigate the use of a multi-speaker TTS system to synthesize speech in support of speaker recognition. In this study we focus the analysis on tasks where a relatively small number of speakers is available for training. We observe on our datasets that TTS synthesized speech improves cross-domain speaker recognition performance and can be combined effectively with multi-style training. Additionally, we explore the effectiveness of different types of text transcripts used for TTS synthesis. Results suggest that matching the textual content of the target domain is a good practice, and if that is not feasible, a transcript with a sufficiently large vocabulary is recommended.

中文翻译：

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Synth2Aug：使用TTS合成语音进行跨域说话人识别

近年来，文本转语音（TTS）已被用作语音识别的数据增强技术，以帮助弥补训练数据中的不足。相应地，我们调查了使用多说话者TTS系统来合成语音以支持说话者识别的情况。在这项研究中，我们将分析重点放在任务上，这些任务需要相对较少的发言人进行培训。我们在数据集上观察到，TTS合成语音可以提高跨域说话者的识别性能，并且可以有效地与多种风格的训练相结合。此外，我们探索了用于TTS合成的不同类型的文字记录的有效性。结果表明，匹配目标域的文本内容是一种很好的做法，如果这样做不可行，