Turkish spoken question answering: Data generation and performance evaluation

This research is a comprehensive study on the development and evaluation of spoken question answering (SQA) in Turkish. The study systematically examines the challenges and methods involved in adapting question answering (QA) systems to process spoken data in Turkish. It begins with the generation of spoken datasets derived from the English to Turkish machine-translated version of the Stanford Question Answering Dataset (SQuAD) 2.0, called SQuAD-TR, and the Turkish split of Cross-lingual Question Answering Dataset (XQuAD), suitable for both natural and synthetic speech contexts. The basis for the creation of spoken datasets lies the integration of detailed text normalization and text-to-speech (TTS) systems. It continues with the iterative process of fine-tuning and evaluating various state-of-the-art automatic speech recognition (ASR) models in conjunction with the development of a robust QA model capable of accurately interpreting and processing Turkish spoken QA data. The research culminates in a comprehensive evaluation of the overall performance of the SQA system on various spoken and text-based QA datasets. This work not only improves the understanding of spoken QA systems in a multilingual context, but also provides important insights and advances specific to Turkish language processing, thus contributing to the broader field of multilingual natural language processing.