中文标题#
一種在大型語言模型（LLM）驅動應用背景下衡量自動語音識別（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#
自動語音識別（ASR）在人機交互中起着關鍵作用，並作為一系列應用的接口。 傳統上，ASR 性能是通過詞錯誤率（WER）來評估的，這是一種量化生成轉錄文中插入、刪除和替換數量的指標。 然而，隨著大型強大語言模型（LLMs）在各種應用中的核心處理組件日益普及，不同類型的 ASR 錯誤在下游任務中的重要性值得進一步探索。 在本工作中，我們分析了 LLMs 糾正 ASR 引入錯誤的能力，並提出了一種新的度量標準來評估 LLM 驅動應用中的 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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