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In recent years, Text-To-Speech (TTS) has been used as a data augmentationtechnique for speech recognition to help complement inadequacies in thetraining data. Correspondingly, we investigate the use of a multi-speaker TTSsystem to synthesize speech in support of speaker recognition. In this study wefocus the analysis on tasks where a relatively small number of speakers isavailable for training. We observe on our datasets that TTS synthesized speechimproves cross-domain speaker recognition performance and can be combinedeffectively with multi-style training. Additionally, we explore theeffectiveness of different types of text transcripts used for TTS synthesis.Results suggest that matching the textual content of the target domain is agood practice, and if that is not feasible, a transcript with a sufficientlylarge vocabulary is recommended.

Synth2Aug: Cross-domain speaker recognition with TTS synthesized speech