BanglaMUX: enhancing regional dialect detection, transcription and translation performance for low-resource Bangla language

Abstract

Transcription of various Bangla dialect speeches and translation into standard Bangla text can help the marginalized communities to have better access to information while ensuring their voices are acknowledged and represented in larger texts named “BanglaMUX”. The speech data from speakers of low-resource languages has been preprocessed in chunks with VAD and text data in standard Bangla are trained using techniques of deep learning and acoustic modeling for speech recognition and machine translation models. These models are then adapted to handle unique linguistic features and lexicons of the low-resource languages along with the fine-tuning of parameters and algorithms for enhanced accuracy and robustness. The developed model is expected to capture the intended meaning of the speech and the model can be further enhanced and extended to accommodate new languages and accents in different regions of the world where people can embrace and appreciate the differences in the language rather than limiting themselves. Therefore, this research topic can assist in decreasing the difficulties of several communication barriers experienced by the speakers of various low-resource languages all within a framework.