THE CMU ENTRY TO BLIZZARD MACHINE LEARNING CHALLENGE

The paper describes Carnegie Mellon University's (CMU) entry to the ES-1 sub-task of the Blizzard Machine Learning Speech Synthesis Challenge 2017. The submitted system is a parametric model trained to predict vocoder parameters given linguistic features. The task in this year's challenge was to synthesize speech from children's audiobooks. Linguistic and acoustic features were provided by the organizers and the task was to find the best performing model. The paper explores various RNN architectures that were investigated and describes the final model that was submitted.