Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices

• URL: http://arxiv.org/abs/2406.02562v1
• Date: Wed, 24 Apr 2024 01:31:39 GMT
• Title: Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices
• Authors: Gwantae Kim, Bokyeung Lee, Donghyeon Kim, Hanseok Ko,
• Abstract summary: 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.
• Score: 28.06179341376626
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 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.

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