提出新评估指标，精准衡量翻译语音同步性。
IsoChronoMeter: A simple and effective isochronic translation evaluation metric
- 基于先进语音合成时长预测器，无需真实数据即可评估翻译同步性。
- 发现当前主流翻译系统在语音同步上存在明显缺陷。
- 适合自动配音、视频字幕等对时间同步要求高的场景研究者使用。
机器翻译已广泛应用于生产系统，服务数百万用户。随着生成式AI的发展，自动配音成为可能。本文强调了等时翻译在自动配音中的重要性，并提出一种名为「IsoChronoMeter」（ICM）的简单有效评估指标。该指标可无需参考数据，通过先进的文本转语音（TTS）时长预测器，以可扩展且资源高效的方式度量翻译的等时性。我们验证了ICM的有效性，并利用其揭示了当前先进翻译系统在等时性方面的不足，表明需要新的方法。代码已开源： https://github.com/braskai/isochronometer。
原文摘要 · Abstract (English)
Machine translation (MT) has come a long way and is readily employed in production systems to serve millions of users daily. With the recent advances in generative AI, a new form of translation is becoming possible - video dubbing. This work motivates the importance of isochronic translation, especially in the context of automatic dubbing, and introduces `IsoChronoMeter' (ICM). ICM is a simple yet effective metric to measure isochrony of translations in a scalable and resource-efficient way without the need for gold data, based on state-of-the-art text-to-speech (TTS) duration predictors. We motivate IsoChronoMeter and demonstrate its effectiveness. Using ICM we demonstrate the shortcomings of state-of-the-art translation systems and show the need for new methods. We release the code at this URL: \url{https://github.com/braskai/isochronometer}.
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