Humor recognition in conversations is a highly challenging task due to its reliance on context, sarcasm, timing, and multimodal cues. While several humour datasets exist for English, resources for Indic languages like Hindi remain little scarce, limiting the development of culturally aware conversational AI systems. To address this gap, we introduce a new multimodal dataset for humour detection from conversational dialogues. The dataset consists of 5203 annotated utterances of 2 season containing 8 episodes each from a popular Indian web series Panchayat. Utterances are kept in their original Devanagari script as spoken in the series, without transliteration or standardization into pure Hindi, to retain the natural conversational tone and cultural nuances. Each utterance is annotated with a binary humour label (Humour / Not Humour) and includes aligned textual, acoustic and visual information, making it suitable for multimodal and context-aware humour research. Our experiments highlight the complementary nature of multimodal signals and the importance of conversational context in understanding Hindi humour. This dataset is expected to promote research on computational humour, sarcasm detection and multimodal dialogue understanding for low-resource languages. The dataset and baselines are available at https://huggingface.co/datasets/Abhis4e/Panchayat-multimodal-dataset and https://github.com/singhbishtabhishek/Multimodal-Dataset.

Low-Rank Adaptation (LoRA) enables efficient fine-tuning of large language models, yet why it works particularly well for code-mixed text remains unexplained. We propose that LoRA’s efficiency stems from geometric structure in multilingual pre-trained models: code-mixed embeddings concentrate in low-dimensional cross-lingual subspaces. Through spectral analysis of mBERT and MuRIL on Hindi-English (Hinglish) data, we establish that pre-trained attention weights have effective ranks of 437–441, while LoRA updates (r = 4,8,16) exhibit ranks of 2.1–5.9—a 136× average compression. Cross-lingual geometry measured via Centered Kernel Alignment shows Hinglish embeddings align strongly with Hindi (CKA=0.279) but weakly with English (0.093), compared to a monolingual baseline of 0.074. Statistical tests (Wilcoxon p < 10−19) and permutation ablations confirm these differences are robust. We interpret the convergence of geometric overlap (3.77× baseline) and empirical compression (136×) as evidence that low-rank adaptation exploits pre-existing multilingual structure. Findings are demonstrated on token-level language identification; extensions to other language pairs and tasks remain open questions.

People belonging to hearing-impaired community feels very uncomfortable while travelling or visiting at Railway Stations without the help of human interpreter. Hearing-impaired people are not able to hear any announcements made at Railway Stations like which train heading to which destination. They remain ignorant about the choosing of platform number or counter number without the help of interpreter. Even they cannot find whether train is on time, delayed or cancelled. The Railway Stations Announcement System for Deaf is a rule-based MT developed. It is the first system developed in the domain of public places to translate all the announcements used at Railway Stations into Indian Sign Language (ISL) synthetic animations. The system is developed using Python and Flask Framework. This Machine Translation system accepts announcements in the form of English text as input and produces Indian Sign Language (ISL) synthetic animations as output.

People belonging to hearing-impaired community feels very uncomfortable while travelling or visiting at airport without the help of human interpreter. Hearing-impaired people are not able to hear any announcements made at airport like which flight heading to which destination. They remain ignorant about the choosing of gate number or counter number without the help of interpreter. Even they cannot find whether flight is on time, delayed or cancelled. The Airport Announcement System for Deaf is a rule-based MT developed. It is the first system developed in the domain of public places to translate all the announcements used at Airport into Indian Sign Language (ISL) synthetic animations. The system is developed using Python and Flask Framework. This Machine Translation system accepts announcements in the form of English text as input and produces Indian Sign Language (ISL) synthetic animations as output.