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TaLK-Corpus: A Regionally Diverse Evaluation Set for Sri Lankan Tamil Speech

This paper introduces the TaLK Corpus, the first speech benchmark corpus for Sri Lankan Tamil Automatic Speech Recognition (ASR) covering speech from 22 administrative districts of Sri Lanka. The corpus contains 1 hour and 33 minutes of speech from 22 native speakers (one per district) and includes rich metadata on demographics, location history, recording conditions, and domain information, along with transcriptions in Tamil script and the International Phonetic Alphabet (IPA). Standardised preprocessing (16 kHz mono WAV format) and segmentation using Silero Voice Activity Detection (VAD) resulted in 1,214 utterances. All recordings were manually transcribed by trained linguists, and MD5-based file naming used to ensure data integrity and consistency. TaLK corpus enables district-wise benchmarking of ASR systems and supports dialect-sensitive evaluation. We establish baseline results for multilingual models (Whisper Large-V3 and Facebook’s MMS) in zero-shot settings. The evaluation reveals substantial performance disparities across districts, highlighting the impact of regional phonological variation in low-resource Sri Lankan Tamil. Although Whisper Large-V3 outperforms MMS overall, it shows considerable variability, with mean Word Error Rates ranging from 0.672 to 0.903 across districts. These findings demonstrate strong regional effects even within a single model. By releasing TaLK-Corpus under the CC-BY-NC 4.0 licence, we aim to support dialect-robust ASR research and foster inclusive speech technologies for Sri Lankan Tamil-speaking communities.