# N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space

## TL;DR

This paper introduces an N-best T5 model that leverages multiple ASR hypotheses and constrained decoding to improve error correction in speech recognition, surpassing previous single-hypothesis methods.

## Contribution

The work presents a novel N-best T5 model fine-tuned for ASR error correction, utilizing richer decoding space and constrained decoding for better guidance.

## Key findings

- Outperforms strong Conformer-Transducer baseline
- Utilizes multiple hypotheses for improved correction
- Employs constrained decoding for better guidance

## Abstract

Error correction models form an important part of Automatic Speech Recognition (ASR) post-processing to improve the readability and quality of transcriptions. Most prior works use the 1-best ASR hypothesis as input and therefore can only perform correction by leveraging the context within one sentence. In this work, we propose a novel N-best T5 model for this task, which is fine-tuned from a T5 model and utilizes ASR N-best lists as model input. By transferring knowledge from the pre-trained language model and obtaining richer information from the ASR decoding space, the proposed approach outperforms a strong Conformer-Transducer baseline. Another issue with standard error correction is that the generation process is not well-guided. To address this a constrained decoding process, either based on the N-best list or an ASR lattice, is used which allows additional information to be propagated.