Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices
<|reference_start|>Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices: 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 known as code-switching, the personalized ASR model is necessary to address such cases. However, current multilingual speech recognition models are limited to recognizing a single language within each utterance. To tackle this problem, we propose code-switching speech recognition models that incorporate fine-tuned monolingual and multilingual speech recognition models. Additionally, we introduce a gated low-rank adaptation(GLoRA) for parameter-efficient fine-tuning with minimal performance degradation. Our experiments, conducted on Korean-English code-switching datasets, demonstrate that fine-tuning speech recognition models for code-switching surpasses the performance of traditional code-switching speech recognition models trained from scratch. Furthermore, GLoRA enhances parameter-efficient fine-tuning performance compared to conventional LoRA.<|reference_end|>
@article{kim2024gated,
    title={Gated Low-rank Adaptation for personalized Code-Switching Automatic
  Speech Recognition on the low-spec devices},
    author={Gwantae Kim, Bokyeung Lee, Donghyeon Kim and Hanseok Ko},
    journal={ICASSP 2024 Workshop(HSCMA 2024) paper},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02562},
    primaryClass={eess.AS cs.AI cs.CL}
}