(5.9% → 1.9% on word error rate (WER) and 0.580 → 0.681 on audio similarity).

3. Voicebox is the first model that can perform high-quality cross-lingual zero-shot TTS across
six languages. It does not use any style labels, pre-trained embedders, or multilingual
samples. Compared to the prior cross-lingual SOTA YourTTS, Voicebox reduces the average
WER from 10.9% to 5.2%, and improves audio similarity from 0.335 to 0.481.

4. Voicebox is capable of infilling speech of any length and outperforms the prior SOTA A3T,
on text guided denoising with -8.8% WER, +0.450 similarity, and +0.80 mean opinion score.
5. Voicebox can generate diverse and realistic speech. An ASR system can be trained solely
on synthetic speech generated by Voicebox, resulting in only 0.4%/1.7% absolute WER
increase on Librispeech test-other/test-clean compared to training on real data. In contrast,
previous TTS models suffer from at least 18.2%/44.5% absolute WER increase.

2 Related Work
Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale