We present Translatotron 2, a neural direct speech-to-speech translation
model that can be trained end-to-end. Translatotron 2 consists of a speech
encoder, a linguistic decoder, an acoustic synthesizer, and a single attention
module that connects them together. Experimental results on three datasets
consistently show that Translatotron 2 outperforms the original Translatotron
by a large margin on both translation quality (up to +15.5 BLEU) and speech
generation quality, and approaches the same of cascade systems. In addition, we
propose a simple method for preserving speakers' voices from the source speech
to the translation speech in a different language. Unlike existing approaches,
the proposed method is able to preserve each speaker's voice on speaker turns
without requiring for speaker segmentation. Furthermore, compared to existing
approaches, it better preserves speaker's privacy and mitigates potential
misuse of voice cloning for creating spoofing audio artifacts.

该研究提出了 Translatotron 2，一种神经直接语音转语音翻译模型，它具备优秀的翻译质量和语音生成质量，并推出了一种简单方法来保留语音翻译时原始说话者声音，与现有方法相比，它更好地保留了说话者的隐私。