Rishabh Kumar
2024
Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using Large Language Models
Rishabh Kumar
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Sabyasachi Ghosh
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Ganesh Ramakrishnan
Findings of the Association for Computational Linguistics: EMNLP 2024
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.
2021
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights
Devaraja Adiga
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Rishabh Kumar
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Amrith Krishna
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Preethi Jyothi
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Ganesh Ramakrishnan
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Pawan Goyal
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
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Co-authors
- Ganesh Ramakrishnan 2
- Sabyasachi Ghosh 1
- Devaraja Adiga 1
- Amrith Krishna 1
- Preethi Jyothi 1
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