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        <title>Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using Large Language Models</title>
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        <namePart type="given">Rishabh</namePart>
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    <abstract>In this work, we address the challenge of cross-lingual proper noun recognition in automatic speech recognition (ASR), where proper nouns in an utterance may originate from a language different from the language in which the ASR system is trained. We enhance the performance of end-to-end ASR systems by instructing a large language model (LLM) to correct the ASR model‘s predictions. The LLM‘s context is augmented with a dictionary of cross-lingual words that are phonetically and graphemically similar to the potentially incorrect proper nouns in the ASR predictions. Our dictionary-based method DiP-ASR (Dictionary-based Prompting for Automatic Speech Recognition) significantly reduces word error rates compared to both the end-to-end ASR baseline and instruction-based prompting of the LLM without the dictionary across cross-lingual proper noun recognition tasks involving three secondary languages.</abstract>
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%0 Conference Proceedings
%T Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using Large Language Models
%A Kumar, Rishabh
%A Ghosh, Sabyasachi
%A Ramakrishnan, Ganesh
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kumar-etal-2024-beyond
%X In this work, we address the challenge of cross-lingual proper noun recognition in automatic speech recognition (ASR), where proper nouns in an utterance may originate from a language different from the language in which the ASR system is trained. We enhance the performance of end-to-end ASR systems by instructing a large language model (LLM) to correct the ASR model‘s predictions. The LLM‘s context is augmented with a dictionary of cross-lingual words that are phonetically and graphemically similar to the potentially incorrect proper nouns in the ASR predictions. Our dictionary-based method DiP-ASR (Dictionary-based Prompting for Automatic Speech Recognition) significantly reduces word error rates compared to both the end-to-end ASR baseline and instruction-based prompting of the LLM without the dictionary across cross-lingual proper noun recognition tasks involving three secondary languages.
%R 10.18653/v1/2024.findings-emnlp.399
%U https://aclanthology.org/2024.findings-emnlp.399/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.399
%P 6821-6828

Markdown (Informal)

[Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using Large Language Models](https://aclanthology.org/2024.findings-emnlp.399/) (Kumar et al., Findings 2024)

• Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using Large Language Models (Kumar et al., Findings 2024)

ACL

• Rishabh Kumar, Sabyasachi Ghosh, and Ganesh Ramakrishnan. 2024. Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6821–6828, Miami, Florida, USA. Association for Computational Linguistics.