Hypernetworks for Personalizing ASR to Atypical Speech
*Equal Contributors Parameter-efficient fine-tuning (PEFT) for personalizing automatic speech recognition (ASR) has recently shown promise for adapting general population models to atypical speech. However, these approaches assume a priori knowledge of the atypical speech disorder being adapted for — the diagnosis of which requires expert knowledge that is not always available. Even given this knowledge, data scarcity and high inter/intra-speaker variability further limit the effectiveness of traditional fine-tuning. To circumvent these challenges, we first identify the minimal set of model…
More than 75 million people speak Telugu, predominantly in India’s southern regions, making it one of the most widely spoken languages in the country. Despite such prevalence, Telugu is considered a low-resource language when it comes to speech AI. This means there aren’t enough hours’ worth of speech datasets to…
Device-directed speech detection (DDSD) is a binary classification task that separates the user’s queries to a voice assistant (VA) from background speech or side conversations. This is important for achieving naturalistic user experience. To this end, we propose knowledge distillation (KD) to enhance DDSD accuracy while ensuring efficient deployment. Specifically,…