Speech-Based Visual Question Answering
Ted Zhang, Dengxin Dai, Tinne Tuytelaars, Marie‐Francine Moens, Luc Van Gool · arXiv (Cornell University) · 2017
This paper introduces speech-based visual question answering (VQA), the task of generating an answer given an image and a spoken question. Two methods are studied: an end-to-end, deep neural network that directly uses audio waveforms as input versus a pipelined approach that performs ASR (Automatic Speech Recognition) on the question, followed by text-based visual question answering. Furthermore, we investigate the robustness of both methods by injecting various levels of noise into the spoken question and find both methods to be tolerate noise at similar levels.