Non-Attentive Tacotron: Robust and controllable neural TTS synthesis including unsupervised duration modelingDownload PDF

28 Sep 2020 (modified: 14 Jan 2021)ICLR 2021 Conference Blind SubmissionReaders: Everyone
  • Keywords: tts, text-to-speech
  • Abstract: This paper presents Non-Attentive Tacotron based on the Tacotron 2 text-to-speech model, replacing the attention mechanism with an explicit duration predictor. This improves robustness significantly as measured by unaligned duration ratio and word deletion rate, two metrics introduced in this paper for large-scale robustness evaluation using a pre-trained speech recognition model. With the use of Gaussian upsampling, Non-Attentive Tacotron achieves a 5-scale mean opinion score for naturalness of 4.41, slightly outperforming Tacotron 2. The duration predictor enables both utterance-wide and per-phoneme control of duration at inference time. When accurate target durations are scarce or unavailable in the training data, we propose a method using a fine-grained variational auto-encoder to train the duration predictor in a semi-supervised or unsupervised manner, with results almost as good as supervised training.
  • One-sentence Summary: Non-Attentive Tacotron replaces the attention mechanism in Tacotron 2 with a duration predictor leading to improved robustness, and can be trained with reasonable performance even without duration labels.
  • Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
  • Supplementary Material: zip
13 Replies