In this work, we explore Parameter-Efficient-Learning (PEL) techniques to
repurpose a General-Purpose-Speech (GSM) model for Arabic dialect
identification (ADI). Specifically, we investigate different setups to
incorporate trainable features into a multi-layer encoder-decoder GSM
formulation under frozen pre-trained settings. Our architecture includes
residual adapter and model reprogramming (input-prompting). We design a
token-level label mapping to condition the GSM for Arabic Dialect
Identification (ADI). This is challenging due to the high variation in
vocabulary and pronunciation among the numerous regional dialects. We achieve
new state-of-the-art accuracy on the ADI-17 dataset by vanilla fine-tuning. We
further reduce the training budgets with the PEL method, which performs within
1.86% accuracy to fine-tuning using only 2.5% of (extra) network trainable
parameters. Our study demonstrates how to identify Arabic dialects using a
small dataset and limited computation with open source code and pre-trained
models.

本文探讨了使用 Parameter-Efficient-Learning 技术将一种通用语音模型重新用于阿拉伯方言识别。通过在预训练设置下引入可训练的特征等不同设置来设计了多层编码器 - 解码器 GSM 架构，包括残差适配器和模型重新编程。针对阿拉伯语言中单词的发音和拼写变化大的问题，设计了标记级映射技术来为 GSM 调节阿拉伯方言识别。在 ADI-17 数据集上，通过普通的微调方法，实现了新的最先进准确率。我们进一步采用 PEL 方法降低了训练预算，只使用了额外网络可训练参数的 2.5% 就达到了与微调相当的准确率。我们的研究表明，使用开源代码和预训练模型，可以在数据集较小和计算资源有限的情况下进行阿拉伯方言识别。