  In healthcare and medical diagnostics, Visual Question Answering (VQA)
mayemergeasapivotal tool in scenarios where analysis of intricate medical
images becomes critical for accurate diagnoses. Current text-based VQA systems
limit their utility in scenarios where hands-free interaction and accessibility
are crucial while performing tasks. A speech-based VQA system may provide a
better means of interaction where information can be accessed while performing
tasks simultaneously. To this end, this work implements a speech-based VQA
system by introducing a Textless Multilingual Pathological VQA (TMPathVQA)
dataset, an expansion of the PathVQA dataset, containing spoken questions in
English, German & French. This dataset comprises 98,397 multilingual spoken
questions and answers based on 5,004 pathological images along with 70 hours of
audio. Finally, this work benchmarks and compares TMPathVQA systems implemented
using various combinations of acoustic and visual features.
