  Spoken language understanding (SLU) systems often exhibit suboptimal
performance in processing atypical speech, typically caused by neurological
conditions and motor impairments. Recent advancements in Text-to-Speech (TTS)
synthesis-based augmentation for more fair SLU have struggled to accurately
capture the unique vocal characteristics of atypical speakers, largely due to
insufficient data. To address this issue, we present a novel data augmentation
method for atypical speakers by finetuning a TTS model, called Aty-TTS. Aty-TTS
models speaker and atypical characteristics via knowledge transferring from a
voice conversion model. Then, we use the augmented data to train SLU models
adapted to atypical speech. To train these data augmentation models and
evaluate the resulting SLU systems, we have collected a new atypical speech
dataset containing intent annotation. Both objective and subjective assessments
validate that Aty-TTS is capable of generating high-quality atypical speech.
Furthermore, it serves as an effective data augmentation strategy, contributing
to more fair SLU systems that can better accommodate individuals with atypical
speech patterns.
