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We present a novel typical-to-atypical voice conversion approach (DuTa-VC),which (i) can be trained with nonparallel data (ii) first introduces diffusionprobabilistic model (iii) preserves the target speaker identity (iv) is awareof the phoneme duration of the target speaker. DuTa-VC consists of three parts:an encoder transforms the source mel-spectrogram into a duration-modifiedspeaker-independent mel-spectrogram, a decoder performs the reverse diffusionto generate the target mel-spectrogram, and a vocoder is applied to reconstructthe waveform. Objective evaluations conducted on the UASpeech show that DuTa-VCis able to capture severity characteristics of dysarthric speech, reservesspeaker identity, and significantly improves dysarthric speech recognition as adata augmentation. Subjective evaluations by two expert speech pathologistsvalidate that DuTa-VC can preserve the severity and type of dysarthria of thetarget speakers in the synthesized speech.

DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model