Controllable Emphasis with zero data for text-to-speech
Arnaud Joly Marco Nicolis Ekaterina Peterova Alessandro Lombardi Ammar Abbas Arent van Korlaar Aman Hussain Parul Sharma Alexis Moinet Mateusz Lajszczak Penny Karanasou Antonio Bonafonte Thomas Drugman Elena Sokolova
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.