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