Time out of Mind: Generating Emotionally Conditioned Rate of Speech

Voice synthesis has seen significant improvements in the past decade resulting in highly intelligible voices. Further investigations have resulted in models that can produce variable speech, including conditional emotional expression. The problem lies, however, in a focus on phrase level modifications and prosodic vocal features. Using the CREMA-D dataset we have trained a GAN conditioned on emotion to generate worth lengths for a given input text. These word lengths are relative to neutral speech and can be provided, through speech synthesis markup language (SSML) to a text to speech (TTS) system to generate more expressive speech. We were able to achieve better performances on objective measures for neutral speech, and better time alignment for happy speech when compared to an out of box model.