Controllable Emphasis with zero data for text-to-speech

A popular approach consists in recording a smaller dataset featuring the desired emphasis effect in addition to the main We present a scalable method to produce high quality emphasis'neutral' recordings, and having the model learn the particular for text-to-speech (TTS) that does not require recordings or prosody associated with the emphasized words (see [5, 6, 7, 8] annotations. Many TTS models include a phoneme duration for recent examples). We build one such model as our upper model. A simple but effective method to achieve emphasized anchor, as detailed in section 2.1 speech consists in increasing the predicted duration of the emphasised While this technique works well for the speaker for which word. We show that this is significantly better than'emphasis recordings' are available, it does not directly scale spectrogram modification techniques improving naturalness by to new speakers or different languages. An alternative technique 7.3% and correct testers' identification of the emphasized word adopted with varying degrees of success consists in annotating in a sentence by 40% on a reference female en-US voice.