用Flan-T5改进语音识别后纠错，提升转录准确性和语法正确性。
FlanEC: Exploring Flan-T5 for Post-ASR Error Correction
- 基于Flan-T5的编码器-解码器模型，融合n-best候选句进行纠错
- 在HyPoradise数据集上显著提升转录准确率与语法质量
- 适合需要高精度语音转录的应用场景，如医疗、法律领域
本文提出一种基于Flan-T5的编码器-解码器模型，用于自动语音识别（ASR）后的生成式语音错误纠正（GenSEC），命名为FlanEC。通过利用ASR模型输出的n-best候选列表，将多个可能的转录结果映射为单一更优句子，以提升最终转录的语法正确性、准确性和语言流畅性。我们研究了训练数据规模扩大和多样化数据引入对纠错效果的影响，并在HyPoradise数据集上进行了全面评估。实验还分析了不同设置下的模型可扩展性与效率，验证了指令微调的编码器-解码器模型在该任务中的潜力。
原文摘要 · Abstract (English)
In this paper, we present an encoder-decoder model leveraging Flan-T5 for post-Automatic Speech Recognition (ASR) Generative Speech Error Correction (GenSEC), and we refer to it as FlanEC. We explore its application within the GenSEC framework to enhance ASR outputs by mapping n-best hypotheses into a single output sentence. By utilizing n-best lists from ASR models, we aim to improve the linguistic correctness, accuracy, and grammaticality of final ASR transcriptions. Specifically, we investigate whether scaling the training data and incorporating diverse datasets can lead to significant improvements in post-ASR error correction. We evaluate FlanEC using the HyPoradise dataset, providing a comprehensive analysis of the model's effectiveness in this domain. Furthermore, we assess the proposed approach under different settings to evaluate model scalability and efficiency, offering valuable insights into the potential of instruction-tuned encoder-decoder models for this task.
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