Hate speech detection is a serious challenge due to the different digital media communication, particularly in low-resource languages. This research focuses on the problem of multimodal hate speech detection by incorporating both textual and audio modalities. In the context of social media platforms, hate speech is conveyed not only through text but also through audios, which may further amplify harmful content. In order to manage the issue, we provide a multiclass classification model that influences both text and audio features to detect and categorize hate speech in low-resource languages. The model uses machine learning models for text analysis and audio processing, allowing it to efficiently capture the complex relationships between the two modalities. Class weight mechanism involves avoiding overfitting. The prediction has been finalized using the majority fusion technique. Performance is measured using a macro average F1 score metric. Three languages—Tamil, Malayalam, and Telugu—have the optimal F1-scores, which are 0.59, 0.52, and 0.33.

Misogyny meme detection is identifying memes that are harmful or offensive to women. These memes can hide hate behind jokes or images, making them difficult to identify. It’s important to detect them for a safer and respectful internet for everyone. Our model proposed a multimodal method for misogyny meme detection in Chinese social media by combining both textual and visual aspects of memes. The training and evaluation data were part of a shared task on detecting misogynistic content. We used a pretrained ResNet-50 architecture to extract visual representations of the memes and processed the meme transcriptions with BERT. The model fused modality-specific representations with a feed-forward neural net for classification. The selected pretrained models were frozen to avoid overfitting and to enhance generalization across all classes, and only the final classifier was fine-tuned on labelled meme recollection. The model was trained and evaluated using test data to achieve a macro F1-score of 0.70345. As a result, we have validated lightweight combining approaches for multimodal fusion techniques on noisy social media and how they can be validated in the context of hostile meme detection tasks.

Speech recognition is a helpful tool for accessing technology and allowing people to interact with technology naturally. This is especially true for people who want to access technology but may encounter challenges interacting with technology in traditional formats. Some examples of these people include the elderly or people from the transgender community. This research presents an Automatic Speech Recognition (ASR) system developed for Tamil-speaking elderly and transgender people who are generally underrepresented in mainstream ASR training datasets. The proposed work used the speech data shared by the task organisers of LT-EDI2025. In the proposed work used the fine-tuned model of OpenAI’s Whisper model with Parameter-Efficient Fine-Tuning (P-EFT) with Low-Rank Adaptation (LoRA) along with SpecAugment, and used the AdamW optimization method. The model’s work led to an overall Word Error Rate (WER) of 42.3% on the untranscribed test data. A key feature of our work is that it demonstrates potential equitable and accessible ASR systems addressing the linguistic and acoustic features of vulnerable groups.