Voice Arena 推出 Turing TTS 基准，最佳模型仅骗过听众约 10%

Drop 2/15 我们是否真的在衡量我们最终希望语音 AI 模型达成的目标？ 最好的 AI 声音正变得越来越难以区分。 是彼此之间难以区分。而不是与人类难以区分。 🚀 隆重推出 Turing TTS：一项衡量人类听众将 AI 生成的语音误认为真实人类语音的频率的评估。 我们用真实人类语音测试了领先的 TTS 模型。即便是最好的模型，也只骗过听众约 10% 的情况。 借助 Turing TTS，我们希望为研究界关于如何衡量语音 AI 进展的更广泛讨论做出贡献。 如果我们的目标之一是开发与人类语音无法区分的语音，那么我们当前的评估方法在多大程度上捕捉到了朝着这一目标取得的进展？ 我们并不声称 Turing TTS 是正确的前进方向，也不声称与人类无法区分应当是每个 TTS 系统的目标。 相反，我们希望这些发现能鼓励社区共同审视：对自然度的盲测成对人类评估是否足够，以及我们可能还需要衡量什么。 结果现已在 @voicearena_ai 上线。

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Drop 2/15 Are we actually measuring the things we ultimately want our voice AI models to achieve? The best AI voices are becoming hard to tell apart. From each other. Not from humans. 🚀 Introducing Turing TTS: an evaluation that measures how often human listeners mistake AI-generated speech for real human speech. We tested leading TTS models against real human speech. Even the best model fooled listeners only ~10% of the time. With Turing TTS, we hope to contribute to a broader discussion within the research community about how we measure progress in voice AI. If one of our goals is to develop speech that is indistinguishable from human speech, how well do our current evaluation methods capture progress toward that goal? We don’t claim that Turing TTS is the right way forward, or that human indistinguishability should be the goal of every TTS system. Rather, we hope these findings encourage the community to collectively examine whether blind pairwise human evaluations of naturalness are sufficient, and what else we might need to measure. Results are now live on @voicearena_ai.