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Neural text-to-speech (TTS) has achieved human-like synthetic speech forsingle-speaker, single-language synthesis. Multilingual TTS systems are limitedto resource-rich languages due to the lack of large paired text andstudio-quality audio data. In most cases, TTS systems are built using a singlespeaker's voice. However, there is growing interest in developing systems thatcan synthesize voices for new speakers using only a few seconds of theirspeech. This paper presents ZMM-TTS, a multilingual and multispeaker frameworkutilizing quantized latent speech representations from a large-scale,pre-trained, self-supervised model. Our paper is the first to incorporate therepresentations from text-based and speech-based self-supervised learningmodels into multilingual speech synthesis tasks. We conducted comprehensivesubjective and objective evaluations through a series of experiments. Our modelhas been proven effective in terms of speech naturalness and similarity forboth seen and unseen speakers in six high-resource languages. We also testedthe efficiency of our method on two hypothetical low-resource languages. Theresults are promising, indicating that our proposed approach can synthesizeaudio that is intelligible and has a high degree of similarity to the targetspeaker's voice, even without any training data for the new, unseen language.

ZMM-TTS: Zero-shot Multilingual and Multispeaker Speech Synthesis Conditioned on Self-supervised Discrete Speech Representations