Italy exhibits rich linguistic diversity across its territory due to the
distinct regional languages spoken in different areas. Recent advances in
self-supervised learning provide new opportunities to analyze Italy's
linguistic varieties using speech data alone. This includes the potential to
leverage representations learned from large amounts of data to better examine
nuances between closely related linguistic varieties. In this study, we focus
on automatically identifying the geographic region of origin of speech samples
drawn from Italy's diverse language varieties. We leverage self-supervised
learning models to tackle this task and analyze differences and similarities
between Italy's regional languages. In doing so, we also seek to uncover new
insights into the relationships among these diverse yet closely related
varieties, which may help linguists understand their interconnected evolution
and regional development over time and space. To improve the discriminative
ability of learned representations, we evaluate several supervised contrastive
learning objectives, both as pre-training steps and additional fine-tuning
objectives. Experimental evidence shows that pre-trained self-supervised models
can effectively identify regions from speech recording. Additionally,
incorporating contrastive objectives during fine-tuning improves classification
accuracy and yields embeddings that distinctly separate regional varieties,
demonstrating the value of combining self-supervised pre-training and
contrastive learning for this task.

通过利用自我监督学习模型，本研究自动识别意大利不同语言方言的语音样本的地理来源，分析区域语言之间的差异和相似性，揭示这些多样而又密切相关方言之间的关系并对其演化和区域发展进行理解。通过评估多种监督对比学习目标作为预训练和额外的微调目标，实验证明预训练的自我监督模型能够有效地识别语音录制的地区，并结合对比学习在微调中创造出能够明确分开各个区域语言方言的嵌入，展示了结合自我监督预训练和对比学习对于这一任务的价值。

意大利语言变体的语音分析

Speech Analysis of Language Varieties in Italy

The amount of news being consumed online has substantially expanded in recent
years. Fake news has become increasingly common, especially in regional
languages like Malayalam, due to the rapid publication and lack of editorial
standards on some online sites. Fake news may have a terrible effect on
society, causing people to make bad judgments, lose faith in authorities, and
even engage in violent behavior. When we take into the context of India, there
are many regional languages, and fake news is spreading in every language.
Therefore, providing efficient techniques for identifying false information in
regional tongues is crucial. Until now, little to no work has been done in
Malayalam, extracting features from multiple modalities to classify fake news.
Multimodal approaches are more accurate in detecting fake news, as features
from multiple modalities are extracted to build the deep learning
classification model. As far as we know, this is the first piece of work in
Malayalam that uses multimodal deep learning to tackle false information.
Models trained with more than one modality typically outperform models taught
with only one modality. Our study in the Malayalam language utilizing
multimodal deep learning is a significant step toward more effective
misinformation detection and mitigation.

通过多模态深度学习，在马拉雅拉姆语中进行的研究是更加有效地检测和缓解虚假信息的重要一步。