中文标题#
一种在大型语言模型（LLM）驱动应用背景下衡量自动语音识别（ASR）模型性能的方法
英文标题#
An approach to measuring the performance of Automatic Speech Recognition (ASR) models in the context of Large Language Model (LLM) powered applications
中文摘要#
自动语音识别（ASR）在人机交互中起着关键作用，并作为一系列应用的接口。 传统上，ASR 性能是通过词错误率（WER）来评估的，这是一种量化生成转录文中插入、删除和替换数量的指标。 然而，随着大型强大语言模型（LLMs）在各种应用中的核心处理组件日益普及，不同类型的 ASR 错误在下游任务中的重要性值得进一步探索。 在本工作中，我们分析了 LLMs 纠正 ASR 引入错误的能力，并提出了一种新的度量标准来评估 LLM 驱动应用中的 ASR 性能。
英文摘要#
Automatic Speech Recognition (ASR) plays a crucial role in human-machine interaction and serves as an interface for a wide range of applications. Traditionally, ASR performance has been evaluated using Word Error Rate (WER), a metric that quantifies the number of insertions, deletions, and substitutions in the generated transcriptions. However, with the increasing adoption of large and powerful Large Language Models (LLMs) as the core processing component in various applications, the significance of different types of ASR errors in downstream tasks warrants further exploration. In this work, we analyze the capabilities of LLMs to correct errors introduced by ASRs and propose a new measure to evaluate ASR performance for LLM-powered applications.
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