Early Results from Automating Voice-based Question-Answering Services Among Low-income Populations in India

Abstract

Question-answering systems where users can ask questions based on emergent needs which are then answered by experts or peers, have emerged as an important information seeking modality on digital platforms. Automating this process has been an active area of research since many years, to identify relevant answers from pre-existing question-answer databases. We report on the feasibility of running automated question-answering systems in the context of rural and less-literate users in India, accessed through IVR (Interactive Voice Response) systems. We use commercial speech recognition APIs to convert audio questions asked by users into their equivalent transcripts in real time, in Hindi, and use deep-learning based architectures to retrieve corresponding candidate answers which are instantly played to the users. We report several insights from an earlier phase of running question-answering programmes through a manual operation, to how it was transitioned to an automated setup, and document the user experiences during this journey.