United States

Tamil has a lot of internal variability, including the way it is used in casual conversations, code mixing, and phonetic differences in the way it is spoken in different regions, making it quite difficult to transcribe the spoken word and classify the dialects. In order to address these challenges, our paper presents the system developed by the CUET_InferX team for the Shared Task on Dialect Based Speech Recognition and Classification in Tamil, which was part of DravidianLangTech@ACL 2026. For Subtask 2 (ASR), our proposed system is based on a dual-architecture design that incorporates a fine-tuned Whisper-large-v3 model with Low-Rank Adaptation (LoRA) and a Wav2Vec2 XLSR-53 model, topped with a KenLM statistical language model for n-gram phonetic correction. Our ASR system resulted in a Word Error Rate (WER) of 0.54, which earned us 2nd position for Subtask 2. For Subtask 1 (Speech-Based Dialect Classification), our proposed system is based on a text-based weighted ensemble of IndicBERT, MuRIL, XLM-RoBERTa, and TamilBERT models, which is completely dependent on our ASR system&#39;s transcription outputs. Our proposed system achieved a Macro F1 score of 0.22, which earned us 9th position for Subtask 1.

ACL 2026

CUET_InferX@DravidianLangTech 2026: Shared Task on Dialect Based Speech Recognition and Classification in Tamil

tamil dialects

dialect classification

automatic speech recognition

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Tamil has a lot of internal variability, including the way it is used in casual conversations, code mixing, and phonetic differences in the way it is spoken in different regions, making it quite difficult to transcribe the spoken word and classify the dialects. In order to address these challenges, our paper presents the system developed by the CUET_InferX team for the Shared Task on Dialect Based Speech Recognition and Classification in Tamil, which was part of DravidianLangTech@ACL 2026. For Subtask 2 (ASR), our proposed system is based on a dual-architecture design that incorporates a fine-tuned Whisper-large-v3 model with Low-Rank Adaptation (LoRA) and a Wav2Vec2 XLSR-53 model, topped with a KenLM statistical language model for n-gram phonetic correction. Our ASR system resulted in a Word Error Rate (WER) of 0.54, which earned us 2nd position for Subtask 2. For Subtask 1 (Speech-Based Dialect Classification), our proposed system is based on a text-based weighted ensemble of IndicBERT, MuRIL, XLM-RoBERTa, and TamilBERT models, which is completely dependent on our ASR system's transcription outputs. Our proposed system achieved a Macro F1 score of 0.22, which earned us 9th position for Subtask 1.

workshop paper

The low-resource dialectal Automatic Speech Recognition (ASR) in languages like Tamil is a critical issue because of phonological differences, lack of labeled data and because of the differences in the acoustic of speech patterns among regions. This paper will introduce a dialect-conscious Tamil ASR model that is trained on the Conformer-CTC-BPE-Large framework via the NVIDIA NeMo framework. This model is an integration of convolutional subsampling, multi-head self-attention, and Connectionist Temporal Classification (CTC) decoding along with a BPE tokenizer to make possible both efficient end-to-end speech recognition. The system is tested on the audio recordings of dialectal Tamil, in which mono-channel audio normalization and batch transcription are used. Our findings indicate that even using large pretrained Conformer models, dialectal ASR tasks are successfully implemented even in zero-shot. Transcriptions generated are examined and the challenges associated with the dialectal differences and acoustic models, and we comment on the possible future directions of enhancing data-efficient adaptation in low-resource speech recognition.

Dialectmind@DravidianLang Tech 2026:Zero-Shot Dialectal Tamil Automatic Speech Recognition Using a Large Pretrained Conformer Model

This paper presents our submission to the Depression Detection in Dravidian Languages shared task at DravidianLangTech 2026. We investigate three complementary modeling paradigms for speech-based depression detection in Tamil and Malayalam: (i) acoustic feature engineering using MFCC and prosodic features with a Support Vector Machine (SVM) classifier, (ii) an image-based convolutional neural network (CNN) trained on Mel-spectrogram representations, and (iii) a text-based transformer model leveraging Whisper-generated transcripts fine-tuned with XLM-RoBERTa. Experimental results demonstrate that transformer-based textual modeling substantially outperforms acoustic-only approaches, highlighting the strong discriminative power of lexical information in the dataset. We provide a comparative analysis of the three approaches and discuss limitations and future research directions.

SERENE@DravidianLangTech 2026: Multimodal Approaches for Depression Detection in Dravidian Speech: Acoustic, Spectrogram, and Transformer-Based Models

The rapid growth of social media networks faces challenges in the classification of mul tilingual and code-mixed data.A task is shared by Political Multiclass Sentiment Analysis of Tamil X (Twitter)-DravidianLangTech@ACL 2026 to classify the political text.For the above task, we proposed two solutions to compare a traditional machine learning and the trans former based model.First we developed a Baseline traditional Support vector Machine model using the TF-IDF features.To capture the deeper information from the text we devel oped an XLM-RoBERT model where we used minimal pre-processing technique.The Result shows us that the transformer-based performs well compared to the traditional baseline model with the macro F1 score of 0.3738.The Study highlights the importance of robust multi-class social media political text classification.

Trailblazer@DravidianLangTech 2026:A Comparative Study of TF-IDF SVM and XLM-RoBERTa for Political Multiclass Text Classification.

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.

Azrael@DravidianLangTech 2026:Dialect-Sensitive Automatic Speech Recognition and Classification for Tamil

Depression is a major mental health concern that can be reflected through subtle changes in speech patterns, prosody, and vocal characteristics. In low-resource and multilingual settings, depression detection from speech may become particularly more challenging. In this work, we present our system for the Shared Task on Depression Detection from Malayalam and Tamil. We explored both handcrafted acoustic features (MFCC) and pretrained speech representations (Wav2Vec2) for depression detection, along with a simple fusion strategy to examine their complementary strengths. Our observations showed that Wav2Vec2 generalized better for Malayalam, whereas for Tamil, a validation-tuned probability fusion performed best. The final system achieved macro-F1 scores of 99.5% for Malayalam and 88.6% for Tamil, securing 3rd place in both tasks.

TriVector@DravidianLangTech 2026: Depression Detection from Tamil and Malayalam Speech with Speaker-Independent Evaluation using MFCC and Wav2Vec2

This paper presents an overview of the Shared Task on Prompt Recovery for Large Language Models (LLMs) in Telugu, organized as part of DravidianLangTech @ ACL 2026. The task focuses on identifying the underlying communicative style of Telugu text excerpts, framed as a nine-class single-label classification problem covering Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative, and Persuasive tones. The dataset was constructed by collecting Telugu YouTube comments and generating style-modified variants using an LLM, resulting in 3,000 training instances, 300 validation samples, and 301 test samples. A total of 52 teams registered for the shared task, with 13 teams submitting valid system predictions. Systems explored diverse approaches, including transformer-based fine-tuning (IndicBERT, MuRIL, XLM-R), ensemble and stacking methods, pairwise modeling strategies, curriculum learning, and few-shot large language model prompting. Evaluation was conducted using Macro F1-score as the primary metric. The top-performing system achieved a Macro F1-score of 0.2987. Overall results indicate that Telugu prompt-style recovery remains a challenging problem, particularly due to stylistic overlap and high lexical similarity across classes.

Shared Task on Prompt Style Recovery for Large Language Models in Telugu

This paper presents our systems and results for the Hope Speech Detection in Code-Mixed Tulu Language shared task at the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages (DravidianLangTech-2026). We trained an XLM-RoBERTa-based text classification system for detecting hope speech in code-mixed Tulu social media comments. We compared this organically adapted hope speech detection model with our baseline model. On the development set, the organically adapted model outperformed the baseline system. While our submitted systems performed more modestly on the official test set, these results suggest that further adapting XLM-RoBERTa on organically collected Tulu social media text containing code-mixed and mixed-script variation can improve hope speech detection in code-mixed Tulu.

cantnlp@DravidianLangTech 2026: organic domain adaptation improves multi-class hope speech detection in Tulu

Depression is one of the most common mental health problems in the world. It affects a person’s emotions, thinking, energy levels, and daily life. Early detection of depression is very important to provide timely support and treatment. While many studies focus on identifying depression from text, speech also carries important emotional and psychological signals that are often not fully explored. This paper presents an overview of the shared task on Depression Detection in Dravidian Languages (DD- DL). The task focuses on identifying signs of depression from speech data in two low-resource Dravidian languages: Tamil and Malayalam. Participants were provided with curated training datasets and were asked to build systems to classify speech samples as Depressed or Non-Depressed. The shared task includes two subtasks: (1) Depression detection in Tamil and (2) Depression detection in Malayalam. Participants applied various machine learning and deep learning approaches to model the acoustic and linguistic characteristics of speech. All submissions were evaluated using the macro-F1 score, which ensures fair performance measurement across classes

Shared Task on Depression Detection from Malayalam and Tamil Speech Data

Our proposal for the Dravidian LangTech 2026 Tamil Political Sentiment Analysis job is outlined in this document. Seven categories—substantiated, sarcastic, opinionated, positive, negative, neutral, and none of the above—should be used to classify Tamil political remarks according to their attitudes. Classifying the sentiments of Tamil political utterances is quite difficult. Furthermore, the emotions associated with various identities are not distributed uniformly. We built an ensemble of two transformer-based techniques, XLM-RoBERTa and IndicBERT, and used 10-fold cross-validation to improve the model’s dependability and prevent overfitting in order to address some of these issues while finishing this research. In order to help the model concentrate more on the challenging examples, used oversampling to address class imbalance and Focal Loss to train the model. In order to improve the representation of sentences, finally averaged the token embeddings.

ByteBuilders@DravidianLangTech 2026: Transformer-Based Weighted Ensemble for Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments

The rapid growth of social media platforms has led to a substantial increase in user-generated content, including abusive and offensive language. Detecting abusive content becomes particularly challenging in low-resource and code-mixed language settings such as Telugu-English social media text. Code-mixed content involves transliteration, inconsistent spelling variations, informal expressions, and frequent language switching within a single sentence. This paper focuses on detecting abusive content in Telugu-English code-mixed comments using both traditional machine learning and transformer-based deep learning models. The proposed approach incorporates preprocessing strategies to normalize transliterations and spelling variations, hybrid feature extraction techniques combining TF-IDF and FastText embeddings, and fine-tuning of multilingual transformer models. The study addresses challenges such as morphological complexity, contextual ambiguity, and limited annotated data in low-resource NLP environments.

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