# PAMA-TTS: Progression-Aware Monotonic Attention for Stable Seq2Seq TTS With Accurate Phoneme Duration Control

arXiv: https://arxiv.org/abs/2110.04486 · HTML: https://mlsanity.com/paper/2110.04486/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 9 Oct 2021.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [LPCNet: Improving Neural Speech Synthesis Through Linear Prediction](https://mlsanity.com/paper/1810.11846/) (2018)
- [End-to-End Adversarial Text-to-Speech](https://mlsanity.com/paper/2006.03575/) (2020)
- [Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling](https://mlsanity.com/paper/2010.04301/) (2020)
- [Parallel Tacotron: Non-Autoregressive and Controllable TTS](https://mlsanity.com/paper/2010.11439/) (2020)
- [Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis](https://mlsanity.com/paper/1910.10288/) (2019)
- [Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTS](https://mlsanity.com/paper/1906.00672/) (2019)
- [DurIAN: Duration Informed Attention Network For Multimodal Synthesis](https://mlsanity.com/paper/1909.01700/) (2019)
- [Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling](https://mlsanity.com/paper/2103.14574/) (2021)
- [AlignTTS: Efficient Feed-Forward Text-to-Speech System without Explicit Alignment](https://mlsanity.com/paper/2003.01950/) (2020)
- [Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis](https://mlsanity.com/paper/1807.06736/) (2018)
- [JDI-T: Jointly trained Duration Informed Transformer for Text-To-Speech without Explicit Alignment](https://mlsanity.com/paper/2005.07799/) (2020)
- [VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis](https://mlsanity.com/paper/2107.03298/) (2021)

## Abstract (arXiv)

Sequence expansion between encoder and decoder is a critical challenge in sequence-to-sequence TTS. Attention-based methods achieve great naturalness but suffer from unstable issues like missing and repeating phonemes, not to mention accurate duration control. Duration-informed methods, on the contrary, seem to easily adjust phoneme duration but show obvious degradation in speech naturalness. This paper proposes PAMA-TTS to address the problem. It takes the advantage of both flexible attention and explicit duration models. Based on the monotonic attention mechanism, PAMA-TTS also leverages token duration and relative position of a frame, especially countdown information, i.e. in how many future frames the present phoneme will end. They help the attention to move forward along the token sequence in a soft but reliable control. Experimental results prove that PAMA-TTS achieves the highest naturalness, while has on-par or even better duration controllability than the duration-informed model.