# Semi-supervised learning for continuous emotional intensity controllable speech synthesis with disentangled representations

arXiv: https://arxiv.org/abs/2211.06160 · HTML: https://mlsanity.com/paper/2211.06160/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 11 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality](https://mlsanity.com/paper/2205.04421/) (2022)
- [Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset](https://mlsanity.com/paper/2010.14794/) (2020)
- [Controllable Emotion Transfer For End-to-End Speech Synthesis](https://mlsanity.com/paper/2011.08679/) (2020)
- [Emotional End-to-End Neural Speech Synthesizer](https://mlsanity.com/paper/1711.05447/) (2017)
- [Fine-grained Emotion Strength Transfer, Control and Prediction for Emotional Speech Synthesis](https://mlsanity.com/paper/2011.08477/) (2020)
- [MsEmoTTS: Multi-scale emotion transfer, prediction, and control for emotional speech synthesis](https://mlsanity.com/paper/2201.06460/) (2022)
- [Emotional speech synthesis with rich and granularized control](https://mlsanity.com/paper/1911.01635/) (2019)
- [Multi-SpectroGAN: High-Diversity and High-Fidelity Spectrogram Generation with Adversarial Style Combination for Speech Synthesis](https://mlsanity.com/paper/2012.07267/) (2020)
- [Speech Synthesis with Mixed Emotions](https://mlsanity.com/paper/2208.05890/) (2022)
- [Exploring Transfer Learning for Low Resource Emotional TTS](https://mlsanity.com/paper/1901.04276/) (2019)
- [End-to-End Emotional Speech Synthesis Using Style Tokens and Semi-Supervised Training](https://mlsanity.com/paper/1906.10859/) (2019)
- [Emotion controllable speech synthesis using emotion-unlabeled dataset with the assistance of cross-domain speech emotion recognition](https://mlsanity.com/paper/2010.13350/) (2020)

## Built on by (most cited first, 2 of 2)

- [UDDETTS: Unifying Discrete and Dimensional Emotions for Controllable Emotional Text-to-Speech](https://mlsanity.com/paper/2505.10599/) (2025)
- [Alignment-Free Text-Audiobox for Voice Dubbing and Full-Duplex Dialogue Synthesis](https://mlsanity.com/paper/2609.03992/) (2026)

## Abstract (arXiv)

Recent text-to-speech models have reached the level of generating natural speech similar to what humans say. But there still have limitations in terms of expressiveness. The existing emotional speech synthesis models have shown controllability using interpolated features with scaling parameters in emotional latent space. However, the emotional latent space generated from the existing models is difficult to control the continuous emotional intensity because of the entanglement of features like emotions, speakers, etc. In this paper, we propose a novel method to control the continuous intensity of emotions using semi-supervised learning. The model learns emotions of intermediate intensity using pseudo-labels generated from phoneme-level sequences of speech information. An embedding space built from the proposed model satisfies the uniform grid geometry with an emotional basis. The experimental results showed that the proposed method was superior in controllability and naturalness.