Sadeque, Farig YousufOhin, Shafin Islam2025-06-302025-06-3020252025-01ID 21241049http://hdl.handle.net/10361/26429Cataloged from PDF version of the thesis.Includes bibliographical references (pages 36-37).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.This study explores the development and implementation of a spoken question answering system for Bangla, using the latest advancements in deep learning. To achieve this, this research addresses two key aspects: text-based QA and spoken QA. For the text-based phase, we fine-tuned and evaluated several LLMs including mBERT, Bangla-BERT, RoBERTa on the SQuAD_bn datast. We also evaluated the performance of GPT-4o, Llama 3 by calculating Zero-shot and Few-shot performance. Notably, the GPT-4o with some limitations achieved state-of-the art results on this dataset by outperforming the existing models. A detailed error analysis revealed the limitations was from the dataset inconsistencies. Facing the lack of a Bangla spoken QA dataset, we created a synthesized dataset called Spoken_SQuAD_bn, derived from the SQuAD_bn dataset using the Google Cloud Text-to-Speech API. We benchmarked this new dataset using Automatic-Speech-Recognition (ASR) followed by LLMs, using the Audio Overlapping Score (AOS) metric along with the EM and F1. It showed a significant performance drop because of the ASR error propagation, highlighting the challenges of spoken QA in Bangla. This work establishes a foundation of Bangla spoken-QA by demonstrating the potential as well as the limitations of LLMs in this domain and provides a valuable benchmark dataset for future works.43 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Spoken question-answering systemBangla languageDeep learningASRLLMLow-resource languagesCognitive learning theory.Thesis