Controllable Emphasis with zero data for text-to-speech
By Arnaud Joly, M. Nicolis, Ekaterina Peterova, A. Lombardi, Ammar Abbas, Arent van Korlaar, Aman Hussain, Parul Sharma, Alexis Moinet, Mateusz Lajszczak and 4 more. 2023
6 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing
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In Artificial Intelligence
- Sri Karlapati
- Alexis Moinet
- Thomas Drugman
- Bartosz Putrycz
- Ewa Muszynska
- Mateusz Lajszczak
- Zhenhua Ling
- Yang Ai
In Experimental and Cognitive Psychology
In Signal Processing
Citations in the corpus
- BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data (2024, background): “Cars displayed in these showrooms with ENERGY-EFFICIENT AND GREEN decals prominently featured across the windshield aren’t announcing environmentalism; they’re virtue signaling.”