Mere account mein kitna balance hai? -- On building voice enabled Banking Services for Multilingual Communities

TL;DR

This paper explores the development of voice-enabled banking services for multilingual communities, focusing on handling code-mixed speech and filled pauses using a Naive Bayes classifier with acoustic phone units.

Contribution

It introduces training strategies for intent recognition in multilingual, code-mixed speech for banking, utilizing the Allosaurus library and a Naive Bayes classifier.

Findings

Effective handling of code-mixed speech in banking applications.

Naive Bayes classifier achieves promising intent recognition results.

Insights into training strategies for multilingual speech systems.

Abstract

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.