  Accurate transcription of Bengali text to the International Phonetic Alphabet
(IPA) is a challenging task due to the complex phonology of the language and
context-dependent sound changes. This challenge is even more for regional
Bengali dialects due to unavailability of standardized spelling conventions for
these dialects, presence of local and foreign words popular in those regions
and phonological diversity across different regions. This paper presents an
approach to this sequence-to-sequence problem by introducing the District
Guided Tokens (DGT) technique on a new dataset spanning six districts of
Bangladesh. The key idea is to provide the model with explicit information
about the regional dialect or "district" of the input text before generating
the IPA transcription. This is achieved by prepending a district token to the
input sequence, effectively guiding the model to understand the unique phonetic
patterns associated with each district. The DGT technique is applied to
fine-tune several transformer-based models, on this new dataset. Experimental
results demonstrate the effectiveness of DGT, with the ByT5 model achieving
superior performance over word-based models like mT5, BanglaT5, and umT5. This
is attributed to ByT5's ability to handle a high percentage of
out-of-vocabulary words in the test set. The proposed approach highlights the
importance of incorporating regional dialect information into ubiquitous
natural language processing systems for languages with diverse phonological
variations. The following work was a result of the "Bhashamul" challenge, which
is dedicated to solving the problem of Bengali text with regional dialects to
IPA transcription https://www.kaggle.com/competitions/regipa/. The training and
inference notebooks are available through the competition link.
