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20232025
most citedPrompting Large Language Models with Speech Recognition Abilities

2 citations · 5 across the 8 of their papers we have counts for

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eess.AS2024

Effective internal language model training and fusion for factorized transducer model

Jinxi Guo, Niko Moritz, Yingyi Ma +6

The internal language model (ILM) of the neural transducer has been widely studied. In most prior work, it is mainly used for estimating the ILM score and is subsequently subtracte…

eess.AS2023

End-to-End Speech Recognition Contextualization with Large Language Models

Egor Lakomkin, Chunyang Wu, Yassir Fathullah +3

In recent years, Large Language Models (LLMs) have garnered significant attention from the research community due to their exceptional performance and generalization capabilities.…

eess.AS20232 cited

Prompting Large Language Models with Speech Recognition Abilities

Yassir Fathullah, Chunyang Wu, Egor Lakomkin +9

Large language models have proven themselves highly flexible, able to solve a wide range of generative tasks, such as abstractive summarization and open-ended question answering. I…

eess.AS20231 cited

Towards Selection of Text-to-speech Data to Augment ASR Training

Shuo Liu, Leda Sarı, Chunyang Wu +4

This paper presents a method for selecting appropriate synthetic speech samples from a given large text-to-speech (TTS) dataset as supplementary training data for an automatic spee…

eess.AS20231 cited

Multi-Head State Space Model for Speech Recognition

Yassir Fathullah, Chunyang Wu, Yuan Shangguan +8

State space models (SSMs) have recently shown promising results on small-scale sequence and language modelling tasks, rivalling and outperforming many attention-based approaches. I…