We present a scalable method to produce high quality emphasis for text-to-speech (TTS) that does not require recordings or annotations. Many TTS models include a phoneme duration model. A simple but effective method to achieve emphasized speech consists in increasing the predicted duration of the emphasised word. We show that this is significantly better than spectrogram modification techniques improving naturalness by $7.3\%$ and correct testers' identification of the emphasized word in a sentence by $40\%$ on a reference female en-US voice. We show that this technique significantly closes the gap to methods that require explicit recordings. The method proved to be scalable and preferred in all four languages tested (English, Spanish, Italian, German), for different voices and multiple speaking styles.

翻译：我们提出一种可扩展的方法，用于在文本到语音（TTS）中生成高质量重音，该方法无需录音或标注。许多TTS模型包含音素时长模型，一种简单而有效的重音语音生成方法是通过增加目标单词的预测时长来实现。实验表明，该方法显著优于频谱图修改技术：在参考女性美式英语语音上，自然度提升7.3%，且测试者正确识别句子中重音单词的比例提高40%。我们证明该技术显著缩小了与需要显式录音方法之间的性能差距。该方法在四种测试语言（英语、西班牙语、意大利语、德语）中均表现出可扩展性，且适用于不同语音和多种说话风格。