研究非裔美国英语发音特点对语音识别准确率的影响
Automatic Speech Recognition of African American English: Lexical and Contextual Effects
- 对比有无语言模型的端到端系统，分析发音简化现象的影响
- 发现发音简化使错误率小幅上升，无语言模型系统更受词汇相似性干扰
- 适合关注语音识别公平性与方言适配的研究者参考
自动语音识别（ASR）模型常难以处理非裔美国英语（AAE）的音位、音系及形态句法特征。本研究聚焦两个关键变量：辅音丛省略（CCR）和ING省略。分析其是否提升语音识别错误率，并考察无外部语言模型（LM）的端到端系统是否更受词汇邻近效应影响，而较少依赖上下文可预测性。使用wav2vec 2.0对区域非裔美国英语语料库（CORAAL）进行转录，结合蒙特利尔强制对齐工具（MFA）与发音扩展检测CCR与ING省略。结果表明，CCR与ING省略对词错误率（WER）存在微小但显著的影响，且无语言模型系统中词汇邻近效应更为突出。
原文摘要 · Abstract (English)
Automatic Speech Recognition (ASR) models often struggle with the phonetic, phonological, and morphosyntactic features found in African American English (AAE). This study focuses on two key AAE variables: Consonant Cluster Reduction (CCR) and ING-reduction. It examines whether the presence of CCR and ING-reduction increases ASR misrecognition. Subsequently, it investigates whether end-to-end ASR systems without an external Language Model (LM) are more influenced by lexical neighborhood effect and less by contextual predictability compared to systems with an LM. The Corpus of Regional African American Language (CORAAL) was transcribed using wav2vec 2.0 with and without an LM. CCR and ING-reduction were detected using the Montreal Forced Aligner (MFA) with pronunciation expansion. The analysis reveals a small but significant effect of CCR and ING on Word Error Rate (WER) and indicates a stronger presence of lexical neighborhood effect in ASR systems without LMs.
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