Data Modeling
Virtual Personal Assistant (VPA) for Bengali language with BanglaSTT (Whisper) with RL, BanglaBERT (BERT) and BanglaTTS (SileroTTS)
Sadidul Islam 1, Khandokar A. Mamun 2
Data Modeling 6 (1) 1-8 https://doi.org/10.25163/data.6110803
Submitted: 29 September 2025 Revised: 20 November 2025 Accepted: 25 November 2025 Published: 27 November 2025
Abstract
Voice assistants have, by now, become almost invisible parts of everyday life — quietly setting alarms, answering questions, managing small tasks — yet this convenience has remained, for the most part, unevenly distributed. English-speaking users have benefited considerably; speakers of Bangla, one of the world's most widely spoken languages, largely have not. This review examines the development of a Bengali Virtual Personal Assistant (VPA) that brings together three components — speech recognition, conversational understanding, and speech synthesis — into a single, cohesive pipeline. Specifically, it considers how a Whisper-based Bengali speech-to-text model, a BanglaBERT-driven chatbot, and a SileroTTS-based speech synthesizer can be combined, and, perhaps more interestingly, how Reinforcement Learning might allow such a system to gradually adapt to an individual speaker's voice. That last point matters more than it might first seem: in a country where illiteracy remains common and a meaningful share of the population lives with some form of disability, including speech-related limitations, a one-size-fits-all model risks excluding exactly the people who could benefit most. Drawing on demographic and accessibility data alongside a working prototype, this review traces both what current Bengali NLP tools can already do and where the more difficult, unresolved challenges — personalization, dynamic knowledge, real-world deployment — still lie ahead.
Keywords: —Virtual Personal Assistant, Bengali NLP, Speech to Text, Whisper, BanglaBERT, Text to Speech, Reinforcement Learning, Chatbot
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