Seq-2-Seq based Refinement of ASR Output for Spoken Name Capture
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Abstract (arXiv)
Person name capture from human speech is a difficult task in human-machine conversations. In this paper, we propose a novel approach to capture the person names from the caller utterances in response to the prompt "say and spell your first/last name". Inspired from work on spell correction, disfluency removal and text normalization, we propose a lightweight Seq-2-Seq system which generates a name spell from a varying user input. Our proposed method outperforms the strong baseline which is based on LM-driven rule-based approach.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 28 references that are in the map. First posted on arXiv 29 Mar 2022.
Builds on
12 of 28 references.
References · 12
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.