Video dubbing aims to translate the original speech in a film or television program into the speech in a target language, which can be achieved with a cascaded system consisting of speech recognition, machine translation and speech synthesis. To ensure the translated speech to be well aligned with the corresponding video, the length/duration of the translated speech should be as close as possible to that of the original speech, which requires strict length control. Previous works usually control the number of words or characters generated by the machine translation model to be similar to the source sentence, without considering the isochronicity of speech as the speech duration of words/characters in different languages varies. In this paper, we propose a machine translation system tailored for the task of video dubbing, which directly considers the speech duration of each token in translation, to match the length of source and target speech. Specifically, we control the speech length of generated sentence by guiding the prediction of each word with the duration information, including the speech duration of itself as well as how much duration is left for the remaining words. We design experiments on four language directions (German -> English, Spanish -> English, Chinese <-> English), and the results show that the proposed method achieves better length control ability on the generated speech than baseline methods. To make up the lack of real-world datasets, we also construct a real-world test set collected from films to provide comprehensive evaluations on the video dubbing task.

翻译：视频配音旨在将影视作品中的原始语音翻译为目标语言语音，可通过由语音识别、机器翻译和语音合成组成的级联系统实现。为确保翻译语音与对应视频良好对齐，翻译语音的时长应尽可能接近原始语音时长，这要求严格的时长控制。以往研究通常控制机器翻译模型生成的词或字符数量与源句相似，但未考虑语音的等时性——不同语言中词/字符的语音时长存在差异。本文提出一种专为视频配音任务设计的机器翻译系统，该模型直接在翻译中考虑每个标记的语音时长，以匹配源语言与目标语言语音的时长。具体而言，我们通过利用时长信息引导每个词的预测来控制生成句子的语音时长，该时长信息既包括词本身的语音时长，也包括剩余词所需的时长。我们在四个语言方向（德语→英语、西班牙语→英语、中文↔英语）上设计实验，结果表明所提方法在生成语音的时长控制能力上优于基线方法。为弥补真实数据集的不足，我们还构建了一个从电影中收集的真实世界测试集，以提供对视频配音任务的全面评估。