ASR error correction continues to serve as an important part of
post-processing for speech recognition systems. Traditionally, these models are
trained with supervised training using the decoding results of the underlying
ASR system and the reference text. This approach is computationally intensive
and the model needs to be re-trained when switching the underlying ASR model.
Recent years have seen the development of large language models and their
ability to perform natural language processing tasks in a zero-shot manner. In
this paper, we take ChatGPT as an example to examine its ability to perform ASR
error correction in the zero-shot or 1-shot settings. We use the ASR N-best
list as model input and propose unconstrained error correction and N-best
constrained error correction methods. Results on a Conformer-Transducer model
and the pre-trained Whisper model show that we can largely improve the ASR
system performance with error correction using the powerful ChatGPT model.

本文使用 ChatGPT 模型作为例子，研究其在零样本或一次样本设置中执行 ASR 错误校正的能力，并提出了无约束错误校正和 N-best 约束错误校正方法。结果表明，使用强大的 ChatGPT 模型进行错误校正可以大大提高 ASR 系统性能。

生成式大型语言模型能否执行 ASR 错误校正？

Can Generative Large Language Models Perform ASR Error Correction?

It is difficult for an end-to-end (E2E) ASR system to recognize words such as
named entities appearing infrequently in the training data. A widely used
method to mitigate this issue is feeding contextual information into the
acoustic model. A contextual word list is necessary, which lists all possible
contextual word candidates. Previous works have proven that the size and
quality of the list are crucial. A compact and accurate list can boost the
performance significantly. In this paper, we propose an efficient approach to
obtain a high quality contextual word list for a unified streaming and
non-streaming based Conformer-Transducer (C-T) model. Specifically, we make use
of the phone-level streaming output to first filter the predefined contextual
word list. During the subsequent non-streaming inference, the words in the
filtered list are regarded as contextual information fused into non-casual
encoder and decoder to generate the final recognition results. Our approach can
take advantage of streaming recognition hypothesis, improve the accuracy of the
contextual ASR system and speed up the inference process as well. Experiments
on two datasets demonstrates over 20% relative character error rate reduction
(CERR) comparing to the baseline system. Meanwile, the RTF of our system can be
stabilized within 0.15 when the size of the contextual word list grows over
6,000.

本文提出了一种高效的方法来获取高质量的语境词列表，并将其作为上下文信息融合到非流式编码器和解码器中，以生成最终的识别结果，实验证明与基线系统相比，相对字符错误率降低了 20% 以上。

统一流式和非流式传输器中的上下文偏置的两阶段上下文单词过滤

Two Stage Contextual Word Filtering for Context bias in Unified Streaming and Non-streaming Transducer

Large-scale language models (LLMs) such as GPT-2, BERT and RoBERTa have been
successfully applied to ASR N-best rescoring. However, whether or how they can
benefit competitive, near state-of-the-art ASR systems remains unexplored. In
this study, we incorporate LLM rescoring into one of the most competitive ASR
baselines: the Conformer-Transducer model. We demonstrate that consistent
improvement is achieved by the LLM's bidirectionality, pretraining, in-domain
finetuning and context augmentation. Furthermore, our lexical analysis sheds
light on how each of these components may be contributing to the ASR
performance.

本研究将大型语言模型（如 GPT-2, BERT, RoBERTa）应用于 ASR N-best rescoring，并将其与竞争性高、接近最新技术水平的 ASR 系统相结合。通过实验发现大型语言模型的双向性、预训练、领域内微调和上下文增强对 ASR 性能有显著改善作用，而词汇分析揭示了这些组件如何对 ASR 性能贡献。