Dialect Classification

Dialect classification aims to automatically identify and categorize different linguistic varieties based on textual or acoustic features, with the goal of improving language technologies and understanding linguistic diversity. Current research focuses on developing robust models using techniques like Gaussian Mixture Models, transformer-based architectures, and ensemble methods, often incorporating features such as MFCCs, n-grams, and TF-IDF. Challenges remain in handling out-of-distribution data, addressing biases in models trained primarily on standard varieties, and accurately classifying dialects with subtle differences or multi-label characteristics. This research is crucial for preserving endangered languages, improving the performance of speech and text processing systems, and advancing our understanding of linguistic variation.