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This paper presents Non-Attentive Tacotron based on the Tacotron 2text-to-speech model, replacing the attention mechanism with an explicitduration predictor. This improves robustness significantly as measured byunaligned duration ratio and word deletion rate, two metrics introduced in thispaper for large-scale robustness evaluation using a pre-trained speechrecognition model. With the use of Gaussian upsampling, Non-Attentive Tacotronachieves a 5-scale mean opinion score for naturalness of 4.41, slightlyoutperforming Tacotron 2. The duration predictor enables both utterance-wideand per-phoneme control of duration at inference time. When accurate targetdurations are scarce or unavailable in the training data, we propose a methodusing a fine-grained variational auto-encoder to train the duration predictorin a semi-supervised or unsupervised manner, with results almost as good assupervised training.

Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling