# Explicit Intensity Control for Accented Text-to-speech

arXiv: https://arxiv.org/abs/2210.15364 · HTML: https://mlsanity.com/paper/2210.15364/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [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)
- [Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning](https://mlsanity.com/paper/1907.04448/) (2019)
- [Expressive TTS Training with Frame and Style Reconstruction Loss](https://mlsanity.com/paper/2008.01490/) (2020)
- [L-Vector: Neural Label Embedding for Domain Adaptation](https://mlsanity.com/paper/2004.13480/) (2020)
- [GraphSpeech: Syntax-Aware Graph Attention Network For Neural Speech Synthesis](https://mlsanity.com/paper/2010.12423/) (2020)
- [Accurate Emotion Strength Assessment for Seen and Unseen Speech Based on Data-Driven Deep Learning](https://mlsanity.com/paper/2206.07229/) (2022)

## Built on by (most cited first, 5 of 5)

- [EmoSphere-TTS: Emotional Style and Intensity Modeling via Spherical Emotion Vector for Controllable Emotional Text-to-Speech](https://mlsanity.com/paper/2406.07803/) (2024)
- [AccentBox: Towards High-Fidelity Zero-Shot Accent Generation](https://mlsanity.com/paper/2409.09098/) (2024)
- [Retrieval-Augmented Dialogue Knowledge Aggregation for Expressive Conversational Speech Synthesis](https://mlsanity.com/paper/2501.06467/) (2025)
- [MnTTS2: An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis Dataset](https://mlsanity.com/paper/2301.00657/) (2022)
- [Optimizing Multilingual Text-To-Speech with Accents & Emotions](https://mlsanity.com/paper/2506.16310/) (2025)

## Abstract (arXiv)

Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a variant of the standard version (L1). How to control the intensity of accent in the process of TTS is a very interesting research direction, and has attracted more and more attention. Recent work design a speaker-adversarial loss to disentangle the speaker and accent information, and then adjust the loss weight to control the accent intensity. However, such a control method lacks interpretability, and there is no direct correlation between the controlling factor and natural accent intensity. To this end, this paper propose a new intuitive and explicit accent intensity control scheme for accented TTS. Specifically, we first extract the posterior probability, called as ``goodness of pronunciation (GoP)'' from the L1 speech recognition model to quantify the phoneme accent intensity for accented speech, then design a FastSpeech2 based TTS model, named Ai-TTS, to take the accent intensity expression into account during speech generation. Experiments show that the our method outperforms the baseline model in terms of accent rendering and intensity control.