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Automation of on-call customer support relies heavily on accurate andefficient speech-to-intent (S2I) systems. Building such systems usingmulti-component pipelines can pose various challenges because they requirelarge annotated datasets, have higher latency, and have complex deployment.These pipelines are also prone to compounding errors. To overcome thesechallenges, we discuss an end-to-end (E2E) S2I model for customer supportvoicebot task in a bilingual setting. We show how we can solve E2E intentclassification by leveraging a pre-trained automatic speech recognition (ASR)model with slight modification and fine-tuning on small annotated datasets.Experimental results show that our best E2E model outperforms a conventionalpipeline by a relative ~27% on the F1 score.

End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English