Marathi Dialect Detector:Text and Speech Normalization to Standard Marathi and Hindi

Abstract

Marathi is spoken across Maharashtra and nearby regions in many local forms such as Varhadi, Puneri, Kolhapuri, Marathwada and coastal varieties like Malvani and Konkani influenced Marathi. These dialects differ in pronunciation, vocabulary and sometimes grammar, while most digital systems still expect clean Standard Marathi or Hindi text. When users speak or write in their natural dialect, systems such as educational portals, government websites and chatbots may fail to understand the input or return poor quality output. This paper describes a small but complete framework for handling such cases. The proposed Indic Language Dialect Detector accepts both text and speech in selected Marathi regional dialects and produces normalized text in Standard Marathi and, optionally, in Hindi. For text input, the system fine-tunes a multilingual BERT-style encoder on pairs of dialect sentences and their normalized versions and uses this representation for both dialect classification and text normalization. For speech input, a wav2vec-style automatic speech recognition (ASR) model first converts audio to text, which is then passed through the same BERT-based module. A simple web interface connects these components and lets users type text or record audio and see the dialect label and normalized output. The system is evaluated on a small curated dataset collected from speakers of five dialects. We report dialect classification accuracy, normalization quality using sequence-level metrics, and qualitative examples. Although the dataset is limited, results suggest that combining modern text and speech models with basic rule-based handling is a practical way to support dialect users in low-resource Indian language settings.