突尼斯方言低资源SLU与ASR任务中语音编码器的性能分析
Performance Analysis of Speech Encoders for Low-Resource SLU and ASR in Tunisian Dialect
AI总结：
针对低资源突尼斯阿拉伯语方言及对应低资源SLU、ASR场景，本文对比不同预训练与优化方式的SSL语音编码器在TARIC-SLU数据集上的性能，得出多项重要发现。
摘要：
通过自监督学习（SSL）预训练的语音编码器已在包括口语理解（SLU）和自动语音识别（ASR）等各类下游任务中展现出卓越性能。例如，针对此类任务微调SSL模型已显现出巨大潜力，推动多个高难度数据集上的SOTA性能得到提升。与现有研究不同，本文的贡献在于对比了SSL方法在两种场景下的有效性：一是低资源口语化突尼斯阿拉伯语方言场景，二是该方言与低资源SLU和ASR场景的结合场景，后者仅提供少量语义标注用于微调。我们在TARIC-SLU数据集上使用多种SSL语音编码器开展实验。实验所用语音编码器基于单语或多语言语音数据预训练，其中部分还通过多模态监督师生范式进行了优化，优化过程未使用领域内数据也未使用突尼斯语数据。本研究得出了多项重要发现，将在文中展开讨论。
英文摘要：
Speech encoders pretrained through self-supervised learning (SSL) have demonstrated remarkable performance in various downstream tasks, including Spoken Language Understanding (SLU) and Automatic Speech Recognition (ASR). For instance, fine-tuning SSL models for such tasks has shown significant potential, leading to improvements in the SOTA performance across challenging datasets. In contrast to existing research, this paper contributes by comparing the effectiveness of SSL approaches in the context of (i) the low-resource spoken Tunisian Arabic dialect and (ii) its combination with a low-resource SLU and ASR scenario, where only a few semantic annotations are available for fine-tuning. We conduct experiments using many SSL speech encoders on the TARIC-SLU dataset. We use speech encoders that were pre-trained on either monolingual or multilingual speech data. Some of them have also been refined without in-domain nor Tunisian data through multimodal supervised teacher-student paradigm. This study yields numerous significant findings that we are discussing in this paper.
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。
