# StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis

arXiv: https://arxiv.org/abs/2110.03156 · HTML: https://mlsanity.com/paper/2110.03156/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Generalizing to Unseen Domains via Adversarial Data Augmentation](https://mlsanity.com/paper/1805.12018/) (2018)
- [MOSNet: Deep Learning based Objective Assessment for Voice Conversion](https://mlsanity.com/paper/1904.08352/) (2019)
- [Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset](https://mlsanity.com/paper/2010.14794/) (2020)
- [Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM](https://mlsanity.com/paper/1808.05344/) (2018)
- [Controllable Emotion Transfer For End-to-End Speech Synthesis](https://mlsanity.com/paper/2011.08679/) (2020)
- [Expressive TTS Training with Frame and Style Reconstruction Loss](https://mlsanity.com/paper/2008.01490/) (2020)
- [Fine-grained Emotion Strength Transfer, Control and Prediction for Emotional Speech Synthesis](https://mlsanity.com/paper/2011.08477/) (2020)
- [Emotion controllable speech synthesis using emotion-unlabeled dataset with the assistance of cross-domain speech emotion recognition](https://mlsanity.com/paper/2010.13350/) (2020)
- [Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability](https://mlsanity.com/paper/2104.01408/) (2021)

## Built on by (most cited first, 1 of 1)

- [CASEIN: Cascading Explicit and Implicit Control for Fine-grained Emotion Intensity Regulation](https://mlsanity.com/paper/2307.00020/) (2023)

## Abstract (arXiv)

Recently, emotional speech synthesis has achieved remarkable performance. The emotion strength of synthesized speech can be controlled flexibly using a strength descriptor, which is obtained by an emotion attribute ranking function. However, a trained ranking function on specific data has poor generalization, which limits its applicability for more realistic cases. In this paper, we propose a deep learning based emotion strength assessment network for strength prediction that is referred to as StrengthNet. Our model conforms to a multi-task learning framework with a structure that includes an acoustic encoder, a strength predictor and an auxiliary emotion predictor. A data augmentation strategy was utilized to improve the model generalization. Experiments show that the predicted emotion strength of the proposed StrengthNet are highly correlated with ground truth scores for seen and unseen speech. Our codes are available at: https://github.com/ttslr/StrengthNet.