title: Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices
abstract: In recent times, there has been a growing interest in utilizing personalized large models on low-spec devices, such as mobile and CPU-only devices. However, utilizing a personalized large model in the on-device is inefficient, and sometimes limited due to computational cost. To tackle the problem, this paper presents the weights separation method to minimize on-device model weights using parameter-efficient fine-tuning methods. Moreover, some people speak multiple languages in an utterance, as k
journal: arXiv (Cornell University)