面向低规格设备个性化语码转换自动语音识别的门控低秩适应
Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices
AI总结：
本文提出权重分离方法与门控低秩适应（GLoRA），在低规格设备上实现个性化语码转换自动语音识别，实验证明其优于传统模型且优于常规LoRA。
摘要：
近年来，在移动和仅CPU等低规格设备上利用个性化大模型的兴趣日益浓厚。然而，由于计算成本，在设备上利用个性化大模型效率低下，且有时受到限制。为解决这一问题，本文提出了权重分离方法，利用参数高效微调方法来最小化设备端模型权重。此外，有些人在一次话语中会说多种语言，即语码转换，因此个性化ASR模型有必要处理此类情况。然而，当前的多语言语音识别模型被限制为在每个话语中只能识别单一语言。为解决这一问题，我们提出了结合微调的单语和多语言语音识别模型的语码转换语音识别模型。此外，我们引入了门控低秩适应（GLoRA），用于在性能损失最小的情况下进行参数高效微调。我们在韩英语码转换数据集上进行的实验表明，针对语码转换微调的语音识别模型超越了从零开始训练的传统语码转换语音识别模型的性能。此外，与传统的LoRA相比，GLoRA提升了参数高效微调的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
