End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English
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Abstract (arXiv)
Automation of on-call customer support relies heavily on accurate and efficient speech-to-intent (S2I) systems. Building such systems using multi-component pipelines can pose various challenges because they require large annotated datasets, have higher latency, and have complex deployment. These pipelines are also prone to compounding errors. To overcome these challenges, we discuss an end-to-end (E2E) S2I model for customer support voicebot task in a bilingual setting. We show how we can solve E2E intent classification 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 conventional pipeline by a relative ~27% on the F1 score.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 26 Oct 2022.
Builds on
17 of 31 references.
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5 more
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