标题： 基于数据的发音错误模式发现以实现鲁棒的语音识别 显示英文标题

标题： Data-Driven Mispronunciation Pattern Discovery for Robust Speech Recognition

摘要： 最近机器学习的进展显著提高了语音识别能力，但识别非流利或有口音的说话者语音仍然是一项挑战。 以往的努力依赖于基于规则的发音模式，难以完全捕捉非母语者的错误。 我们提出了两种基于数据的方法，使用语音语料库来自动检测发音错误模式。 通过使用注意力图将非母语音素与对应的母语音素对齐，我们在母语英语数据集上实现了5.7%的语音识别提升，在非母语英语说话者中实现了12.8%的提升，尤其是对韩国说话者。 我们的方法为稳健的自动语音识别（ASR）系统提供了实际的进展，特别是在先前语言知识不适用的情况下。 显示英文摘要

摘要： Recent advancements in machine learning have significantly improved speech recognition, but recognizing speech from non-fluent or accented speakers remains a challenge. Previous efforts, relying on rule-based pronunciation patterns, have struggled to fully capture non-native errors. We propose two data-driven approaches using speech corpora to automatically detect mispronunciation patterns. By aligning non-native phones with their native counterparts using attention maps, we achieved a 5.7% improvement in speech recognition on native English datasets and a 12.8% improvement for non-native English speakers, particularly Korean speakers. Our method offers practical advancements for robust Automatic Speech Recognition (ASR) systems particularly for situations where prior linguistic knowledge is not applicable.