# Nonparallel Emotional Speech Conversion

arXiv: https://arxiv.org/abs/1811.01174 · HTML: https://mlsanity.com/paper/1811.01174/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2018.

## Built on by (most cited first, 17 of 17)

- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [Emotion Intensity and its Control for Emotional Voice Conversion](https://mlsanity.com/paper/2201.03967/) (2022)
- [An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era](https://mlsanity.com/paper/2210.03538/) (2022)
- [Textless Speech Emotion Conversion using Discrete and Decomposed Representations](https://mlsanity.com/paper/2111.07402/) (2021)
- [Limited Data Emotional Voice Conversion Leveraging Text-to-Speech: Two-stage Sequence-to-Sequence Training](https://mlsanity.com/paper/2103.16809/) (2021)
- [Nonparallel Emotional Voice Conversion For Unseen Speaker-Emotion Pairs Using Dual Domain Adversarial Network & Virtual Domain Pairing](https://mlsanity.com/paper/2302.10536/) (2023)
- [DurFlex-EVC: Duration-Flexible Emotional Voice Conversion Leveraging Discrete Representations without Text Alignment](https://mlsanity.com/paper/2401.08095/) (2024)
- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [Towards General-Purpose Text-Instruction-Guided Voice Conversion](https://mlsanity.com/paper/2309.14324/) (2023)
- [StarGAN-based Emotional Voice Conversion for Japanese Phrases](https://mlsanity.com/paper/2104.01807/) (2021)
- [CycleTransGAN-EVC: A CycleGAN-based Emotional Voice Conversion Model with Transformer](https://mlsanity.com/paper/2111.15159/) (2021)
- [Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion](https://mlsanity.com/paper/2312.17508/) (2023)
- [Zero Shot Audio to Audio Emotion Transfer With Speaker Disentanglement](https://mlsanity.com/paper/2401.04511/) (2024)
- [Towards Better Disentanglement in Non-Autoregressive Zero-Shot Expressive Voice Conversion](https://mlsanity.com/paper/2506.04013/) (2025)
- [Neural network algorithm and its application in temperature control of distillation tower](https://mlsanity.com/paper/2101.00582/) (2021)
- [Study on the simulation control of neural network algorithm in thermally coupled distillation](https://mlsanity.com/paper/2102.03506/) (2021)
- [Application of Neural Network Algorithm in Propylene Distillation](https://mlsanity.com/paper/2104.01774/) (2021)

## Abstract (arXiv)

We propose a nonparallel data-driven emotional speech conversion method. It enables the transfer of emotion-related characteristics of a speech signal while preserving the speaker's identity and linguistic content. Most existing approaches require parallel data and time alignment, which is not available in most real applications. We achieve nonparallel training based on an unsupervised style transfer technique, which learns a translation model between two distributions instead of a deterministic one-to-one mapping between paired examples. The conversion model consists of an encoder and a decoder for each emotion domain. We assume that the speech signal can be decomposed into an emotion-invariant content code and an emotion-related style code in latent space. Emotion conversion is performed by extracting and recombining the content code of the source speech and the style code of the target emotion. We tested our method on a nonparallel corpora with four emotions. Both subjective and objective evaluations show the effectiveness of our approach.