Re-evaluate the data of English vowel perception in quiet and noise for native and non-native listeners: Sensitivity and response bias

Abstract

For decades, research on vowel perception has been primarily focused on the percentage correct of identification. Limited studies take listeners’ response bias into consideration and applied signal detection theory (SDT) in the data analysis of vowel identification. The goal of this study was to investigate English vowel identification in quiet, long-term speech-shaped noise (LTSS), and multi-talker babble (MTB) for English-native (EN), Chinese-native in the US (CNU), and Chinese-native in China (CNC) listeners by computing their sensitivity (measured by d’) and response bias (measured by c) using the SDT. Results showed that (1) in all three listening conditions, EN listeners showed higher sensitivity and lower bias than CNU and CNC listeners; (2) in quiet and MTB, CNU group demonstrated higher sensitivity than CNC group with similar sensitivity in LTSSN between two Chinese groups; and (3) in MTB, CNU group had smaller bias than CNC group, while in quiet and LTSSN condition, two Chinese groups showed similar bias. These results suggest that the US residency may not improve Chinese-native listeners’ vowel identification capacity, instead, may shift their response strategy toward native speakers’ pattern. [Work by the University of Texas at Austin Research Grant and China National Natural Science Foundation 31628009.]