As part of DravidianLangTech-2026, we provide a overview of Shared Task on Dialect-based Speech Recognition and Classification in Tamil. Creating reliable system for Tamil dialect identification from audio signals and dialect-aware Automatic Speech Recognition (ASR) is the main goal of the joint work. Dialect-based Tamil Speech Recognition and Tamil Dialect Classification from Speech are the two subtasks that make up the task. 5,134 audio recordings in four Tamil dialects: Southern, Northern, Western, and Central-spanning 9 hours and 22 minutes make up the training dataset. There are 579 audio samples in the test set, totaling almost two hours in length. The shared task involved 17 teams in total. For speech recognition and dialect classification, the top-performing system obtained a Word Error Rate (WER) of 0.51 and a macro F1-score of 0.79, respectively. The findings emphasize the difficulties in understanding Tamil speech due to dialectal diversity and set solid foundations for further study on low-resource dialect-aware ASR systems.

Tamil is a pre-historic language of millions of individuals who live in India, Sri Lanka, and other parts of the world. Consider the variations in accents, vocabulary and even speech rhythm even among the central region, the northern region, the southern region and the western region of Tamil Nadu. Such idiosyncrasies make it difficult to use features such as voice assistants or translation applications to keep up. A feasible system has been developed in this project to manage that challenge. It picks up raw audio files in Tamil, identifies which of the four predominant dialects the speech belongs to and translates that speech into text. Good quality datasets on Tamil dialects are rather rare, due to the lack of resources and interest in languages. There were pre-trained models, namely, XLSR to spot the dialects and Wav2Vec 2.0 to convert speech into text. All in all, this configuration had an accuracy rate of 46 percentage. It was very good at distinguishing between northern and southern, but was somewhat confused between central and west-central-western. In the case of the transcription component, a cursory inspection reveals that it is a reliable process, able to nail down clear speech despite those accent twists. With that said, it is possible to improve it with such details as a more detailed fine-tuning or equalizing the classes of data.

An Automatic Speech Recognition systems for Tamil are designed to convert spoken lan- guage or speech signals into written Tamil text. Seniors go to banks, clinics and authoritative workplaces to address their regular necessities. A lot of older people are not aware of the use of the facilities available in public places or office. They need a person to help them. Like- wise, transgender people are deprived of pri- mary education because of social stigma, so speaking is the only way to help them meet their needs. In order to build speech enabled systems, spontaneous speech data is collected from seniors and transgender people who are deprived of using these facilities for their own benefit. The proposed system is developed with pretraind models are IIT Madras transformer ASR model and akashsivanandan/wav2vec2- large-xls-r-300m-tamil model. Both pretrained models are used to evaluate the test speech ut- terances, and obtainted the WER as 37.7144% and 40.55% respectively.