Phonetic transcription is vital for speech processing and linguistic documentation, particularly in languages like Tamil with complex phonology and dialectal variation. Challenges such as consonant gemination, retroflexion, vowel length, and one-to-many grapheme-phoneme mappings are compounded by limited data on Sri Lankan Tamil dialects. We present a dialect-aware, rule-based transcription tool for Tamil that supports Indian and Jaffna Tamil, with extensions underway for other dialects. Using a two-stage pipeline: Tamil script to Latin, then to IPA with context-sensitive rules, the tool handles dialect shifts. A real-time interface enables dialect selection. Evaluated on a 7,830-word corpus, it achieves 94.54% accuracy for Jaffna Tamil and is higher than other tools like eSpeak NG, advancing linguistic preservation and accessible speech technology for Tamil communities.

Large Language Models (LLMs) have shown strong generalization across tasks in high-resource languages; however, their linguistic competence in low-resource and morphologically rich languages such as Tamil remains largely unexplored. Existing multilingual benchmarks often rely on translated English datasets, failing to capture the language specific linguistic and cultural nuances of the target language. To address this gap, we introduce ILAKKANAM, the first Tamil-specific linguistic evaluation benchmark manually curated using 820 questions from Sri Lankan school-level Tamil subject examination papers spanning Grades 1–13. Each question is annotated by trained linguists under five linguistic categories and a factual knowledge category. We evaluate both closed-source and open-source LLMs using a standardized evaluation pipeline. Our results show that Gemini 2.5 achieves the highest overall performance, while open-source models lag behind, highlighting the gap in linguistic grounding. Category- and grade-wise analyses reveal that all models perform well on lower-grade questions but show a clear decline as the grade level and the linguistic complexity of the questions increase. Further, no strong correlation is observed between a model’s overall performance and its ability to identify linguistic categories, suggesting that performance may be driven by exposure rather than genuine understanding. The code and dataset used in this study are publicly available in our repository, where the dataset consists only of extracted examination questions to mitigate potential data leakage. Keywords: Tamil, Linguistic Benchmark, Linguistic diagnostics, Low-resource language