Kalika Bali, Sunayana Sitaram, Sebastien Cuendet, and Indrani Medhi
Voice user interfaces for ICTD applications have immense potential in their ability to reach to a large illiterate or semi-literate population in these regions where text-based interfaces are of little use. However, building speech systems for a new language is a highly resource intensive task. There have been attempts in the past to develop techniques to circumvent the need for large amounts of data and technical expertise required to build such systems. In this paper we present the development and evaluation of an application specific speech recognizer for Hindi. We use the Salaam method  to bootstrap a high quality speech engine in English to develop a mobile speech based agricultural video search for farmers in India. With very little training data for a 79 word vocabulary we are able to achieve >90% accuracies for test and field deployments. We report some observations from field that we believe are critical to the effective development and usability of a speech application in ICTD.
Publisher ACM Symposium on Computing for Development (ACM DEV)