Rishabh Kumar


2024

pdf bib
Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using Large Language Models
Rishabh Kumar | Sabyasachi Ghosh | 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

pdf bib
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights
Devaraja Adiga | Rishabh Kumar | Amrith Krishna | Preethi Jyothi | Ganesh Ramakrishnan | Pawan Goyal
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021