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.

本文提出了一种可扩展的方法来产生高质量的文本转语音 (TTS) 的强调效果，并在不需要录音或注释的情况下实现，通过预测强调词的持续时间的简单但有效的方法，比光谱图修改技术更好地改善自然度 7.3％，并通过 40％正确测试强调的单词在一个女性 en-US 参考语音的句子中的识别。