3 accepted papers
Most text-to-speech acoustic models, such as WaveNet, Tacotron, ClariNet, etc., use either a phoneme sequence or a letter sequence as the fundamental unit of speech. Although the letter (or grapheme) sequence closely matches the actual runtime input of the TTS system, it often fails to represent the…
The prosodic aspects of speech signals produced by current text-to-speech systems are typically averaged over training material, and as such lack the variety and liveliness found in natural speech. To avoid monotony and averaged prosody contours, it is desirable to have a way of modeling the variati…
We present an extension to the Tacotron speech synthesis architecture that learns a latent embedding space of prosody, derived from a reference acoustic representation containing the desired prosody. We show that conditioning Tacotron on this learned embedding space results in synthesized audio that…