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Can Generative Large Language Models Perform ASR Error Correction?
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ASR error correction is an interesting option for post processing speech recognition system outputs. These error correction models are usually trained in a supervised fashion using the decoding results of a target ASR system. This approach can be computationally intensive and the model is tuned to a specific ASR system. Recently generative large language models (LLMs) have been applied to a wide range of natural language processing tasks, as they can operate in a zero-shot or few shot fashion. In this paper we investigate using ChatGPT, a generative LLM, for ASR error correction. Based on the ASR N-best output, we propose both unconstrained and constrained, where a member of the N-best list is selected, approaches. Additionally, zero and 1-shot settings are evaluated. Experiments show that this generative LLM approach can yield performance gains for two different state-of-the-art ASR architectures, transducer and attention-encoder-decoder based, and multiple test sets.
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Cited by 7 Pith papers
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Can Large Language Models Reliably Correct Errors in Low-Resource ASR? A Contamination-Aware Case Study on West Frisian
LLM generative error correction improves low-resource Frisian ASR performance, with comparable gains on a contamination-controlled offline dataset confirming true correction ability.
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Phonemes vs. Projectors: An Investigation of Speech-Language Interfaces for LLM-based ASR
Phoneme-based interfaces match or surpass projector-based ones for LLM ASR, especially in low-resource languages, and a BPE-phoneme hybrid offers additional improvements.
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A learnable prompt projector added to LLM-based ASR reduces prompt sensitivity, lowers performance variability, and beats the best fixed prompts on four datasets.
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READ is a reference-free ASR hypothesis scorer that measures acoustic discrepancy via conditional likelihood from a pretrained auto-regressive TTS model and yields up to 20% relative error rate reduction when used for...
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Error-Aware TF-IDF Retrieval-Augmented Generation for ASR Error Correction
Introduces error-aware TF-IDF RAG that raises error-aware hit rate from 53.7% to 90.9% and lowers WER from 23.06% to 18.83% on Persian FLEURS data.
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An approach to measuring the performance of Automatic Speech Recognition (ASR) models in the context of Large Language Model (LLM) powered applications
The paper introduces AER, an LLM-judged question-answering metric for evaluating ASR output in LLM applications, and shows it does not correlate strongly with WER.
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Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition
An LLM-based ASR error correction framework with noise-adaptive encoding and dynamic multi-modal fusion reports WER gains, but its fusion weights require ground-truth text at inference.
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