An Evaluation of Neural Vocoder-Based Voice Cloning System for Dysphonia Speech Disorder

Dysphonia is a voice disorder affecting voice quality, quantity, and intensity, occurring at various ages and diverse backgrounds. Dysphonia impacts the difficulty of communication, thereby reducing the overall quality of life. Medical solutions have been proposed to improve the speech quality of individuals with dysphonia. However, these solutions are often limited by considerable expenses and time-consuming procedures. Therefore, alternative solutions are needed to enhance speech quality. The widespread development of technology in various domains can be proposed as an alternative solution. One is speech processing technology using text-to-speech (TTS) with voice cloning techniques. Our work presents the impact of the vocoder in a voice cloning system on the quality of synthesized speech for dysphonia speakers. We compare selected vocoder models based on architecture and performance. Furthermore, we explore the effect of using Speaker Conditionals on the vocoder. We perform an objective evaluation for each vocoder to measure the quality of the models.