  Tremendous progress in speech and language processing has brought language
technologies closer to daily human life. Voice technology has the potential to
act as a horizontal enabling layer across all aspects of digitization. It is
especially beneficial to rural communities in scenarios like a pandemic. In
this work we present our initial exploratory work towards one such direction --
building voice enabled banking services for multilingual societies. Speech
interaction for typical banking transactions in multilingual communities
involves the presence of filled pauses and is characterized by Code Mixing.
Code Mixing is a phenomenon where lexical items from one language are embedded
in the utterance of another. Therefore speech systems deployed for banking
applications should be able to process such content. In our work we investigate
various training strategies for building speech based intent recognition
systems. We present our results using a Naive Bayes classifier on approximate
acoustic phone units using the Allosaurus library.
