Abstract
It is important to make automatic speech recognition (ASR) be inclusive to all users, including those with disorders. Besides model performances, privacy concerns, such as leakage of medical condition, are severe and harmful for this already vulnerable population. Hence, developing privacy-preserving machine learning (PPML) algorithms is important. Recent node cancellation strategies, while repeatedly showing their privacy protection efficacy, involve complex multi-branched structures with manually-tuned thresholds. In this work, we focus on learning ASR for dementia patients without revealing their medical condition. Specifically, we present a dementia attribute cancellation strategy (DACS) that trains a single toggling network in an end-to-end manner to toggle off particular node dimensions at ASR decoding, concealing a subject's dementia status. We show that using DACS can achieve 33% dementia protection efficacy (DPE), and further configuring for higher protection efficacy achieves 44% DPE, with only a slight decrease of 0.1% WER in ASR performance.