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A Multidomain Generative Adversarial Network for Hoarse-to-Normal Voice Conversion.

Minghang Chu¹, Jing Wang¹, Zhiwei Fan¹

¹School of Optoelectronic Science and Engineering, Soochow University, Suzhou, Jiangsu, China.

Journal of Voice : Official Journal of the Voice Foundation

|October 16, 2023

Summary

This study introduces a novel voice conversion method to transform hoarse voices into normal ones, significantly improving speech quality and personalization for patients. The technique enhances naturalness, intelligibility, and content similarity compared to existing methods.

Keywords:

Artificial intelligence Health sciences Hoarse voice conversion Intelligibility Multidomain generative adversarial network Pathological voice

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Area of Science:

Speech processing
Artificial intelligence
Biomedical engineering

Background:

Hoarse voice impairs communication efficiency.
Current treatments like surgery can worsen voice quality, and existing repair techniques are limited.
There is a need for effective methods to restore voice quality for individuals with hoarseness.

Purpose of the Study:

To propose a novel multidomain generative adversarial voice conversion method for hoarse-to-normal voice conversion.
To personalize voices for patients experiencing hoarseness.
To enhance the overall speech quality of hoarse voices.

Main Methods:

Development of a multidomain generative adversarial network (GAN) for voice conversion.
Evaluation using subjective and objective metrics, including spectrum analysis, word error rate, naturalness, intelligibility, and content similarity.
Comparison with existing methods such as Variational Auto-Encoder (VAE), Auto-VC, StarGAN-VC, and CycleVAE.

Main Results:

The proposed method demonstrated superior formant conversion of hoarse voices compared to VAE, Auto-VC, StarGAN-VC, and CycleVAE.
Significant improvements were observed in word error rate, naturalness, intelligibility, and content similarity compared to baseline methods.
ABX results confirmed the method's capability for voice personalization in hoarse patients.

Conclusions:

The novel multidomain GAN-based voice conversion method effectively improves speech quality for hoarse voices.
The proposed approach offers a feasible solution for voice restoration and personalization in clinical applications.
This study highlights the potential of advanced AI techniques in addressing communication impairments due to hoarseness.