Dravid-Tech-Builders@DravidianLangTech 2026: A Comparative Study of Classical and Deep Learning Approaches for Tamil Dialect Classification and Speech Recognition

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Natural Language Processing & Speech Technology · Depth: Expert, medium

Summary

The Dravid-Tech-Builders team, participating in the DravidianLangTech 2026 shared task, conducted a comparative study on Tamil dialect classification and automatic speech recognition (ASR). Their research addressed the challenges of Tamil's dialectal diversity for speech-enabled services. They classified Tamil speech into four regional dialects (Central, Northern, Southern, Western) and performed ASR. The study evaluated four machine learning models (SVM, Random Forest, CNN, CNN+BiLSTM) and two transfer learning models (Wav2Vec2-Base, Wav2Vec2-XLSR-53). For dialect classification, SVM with MFCC features achieved the best performance, yielding a 94.17% macro F1-score and 94.35% validation accuracy. In ASR, Wav2Vec2-XLSR-53 demonstrated effective cross-lingual knowledge transfer with a 15.3% Word Error Rate (WER). A key finding indicates that traditional machine learning approaches with engineered features surpass deep learning methods in low-resource scenarios with limited training data. The project's code is publicly available on GitHub.

Key takeaway

For Machine Learning Engineers developing speech systems for low-resource languages like Tamil, prioritize traditional machine learning models with engineered features over deep learning when training data is limited. Your efforts in feature engineering, particularly with MFCCs for dialect classification, can yield superior results, as demonstrated by SVM's 94.17% F1-score. Additionally, consider leveraging pre-trained cross-lingual models like Wav2Vec2-XLSR-53 for ASR to achieve effective performance, such as a 15.3% WER, even with scarce data.

Key insights

Traditional ML with engineered features outperforms deep learning in low-resource speech tasks.

Principles

• Low-resource speech tasks benefit from engineered features.
• Cross-lingual transfer learning is effective for ASR.
• SVM with MFCCs excels in dialect classification.

Method

The study trained SVM, Random Forest, CNN, and CNN+BiLSTM models for dialect classification and ASR, alongside Wav2Vec2-Base and Wav2Vec2-XLSR-53 for ASR, comparing their performance on Tamil speech.

In practice

• Use SVM with MFCCs for low-resource dialect classification.
• Consider Wav2Vec2-XLSR-53 for cross-lingual ASR.
• Prioritize feature engineering in data-scarce scenarios.

Topics

• Tamil Language Technology
• Dialect Classification
• Automatic Speech Recognition
• Low-Resource NLP
• Wav2Vec2
• MFCC Features
• Machine Learning Models

Code references

• Naveen-Arul/dravid-tech

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.