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Voice synthesis has seen significant improvements in the past decaderesulting in highly intelligible voices. Further investigations have resultedin models that can produce variable speech, including conditional emotionalexpression. The problem lies, however, in a focus on phrase-level modificationsand prosodic vocal features. Using the CREMA-D dataset we have trained a GANconditioned on emotion to generate worth lengths for a given input text. Theseword lengths are relative to neutral speech and can be provided, through speechsynthesis markup language (SSML) to a text-to-speech (TTS) system to generatemore expressive speech. Additionally, a generative model is also trained usingimplicit maximum likelihood estimation (IMLE) and a comparative analysis withGANs is included. We were able to achieve better performances on objectivemeasures for neutral speech, and better time alignment for happy speech whencompared to an out-of-box model. However, further investigation of subjectiveevaluation is required.

Time out of Mind: Generating Rate of Speech conditioned on emotion and speaker