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We present an extension to the Tacotron speech synthesis architecture thatlearns a latent embedding space of prosody, derived from a reference acousticrepresentation containing the desired prosody. We show that conditioningTacotron on this learned embedding space results in synthesized audio thatmatches the prosody of the reference signal with fine time detail even when thereference and synthesis speakers are different. Additionally, we show that areference prosody embedding can be used to synthesize text that is differentfrom that of the reference utterance. We define several quantitative andsubjective metrics for evaluating prosody transfer, and report results withaccompanying audio samples from single-speaker and 44-speaker Tacotron modelson a prosody transfer task.

Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron