E-DGAN: An Encoder-Decoder Generative Adversarial Network Based Method for Pathological to Normal Voice Conversion

doi:10.1109/JBHI.2023.3239551

E-DGAN: An Encoder-Decoder Generative Adversarial Network Based Method for Pathological to Normal Voice Conversion

2023-05-01

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DOI：10.1109/JBHI.2023.3239551

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In recent years, more and more people suffer from voice-related diseases. Given the limitations of current pathological speech conversion methods, that is, a method can only convert a single kind of pathological voice. In this study, we propose a novel Encoder-Decoder Generative Adversarial Network (E-DGAN) to generate personalized speech for pathological to normal voice conversion, which is suitable for multiple kinds of pathological voices. Our proposed method can also solve the problem of improving the intelligibility and personalizing custom speech of pathological voices. Feature extraction is performed using a mel filter bank. The conversion network is an encoder-decoder structure, which is used to convert the mel spectrogram of pathological voices to the mel spectrogram of normal voices. After being converted by the residual conversion network, the personalized normal speech is synthesized by the neural vocoder. In addition, we propose a subjective evaluation metric named content similarity to evaluate the consistency between the converted pathological voice content and the reference content. The Saarbrucken Voice Database (SVD) is used to verify the proposed method. The intelligibility and content similarity of pathological voices are increased by 18.67% and 2.60%, respectively. Besides, an intuitive analysis based on a spectrogram was done and a significant improvement was achieved. The results show that our proposed method can improve the intelligibility of pathological voices and personalize the conversion of pathological voices into the normal voices of 20 different speakers. Our proposed method is compared with five other pathological voice conversion methods, and our proposed method has the best evaluation results.

Keyword:

Pathology

Maintenance engineering

Generators

Deep learning

Generative adversarial networks

Feature extraction

Spectrogram

Pathological voice

pathological voice conversion

Encoder-Decoder Generative Adversarial Network (E-DGAN)

intelligibility

content similarity

E-DGAN: An Encoder-Decoder Generative Adversarial Network Based Method for Pathological to Normal Voice Conversion

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