Speech-in-noise perception for new sentence-recognition materials: Normative data for a diverse non-native listener population

Abstract

Even though the US population is becoming more diverse, there are no normalized sentence recognition tests for non-native English listeners. Recently, a large set of new test materials aimed at assessing speech recognition and hearing abilities for native and non-native listeners were developed by Smiljanic and Calandruccio [J. Acoust. Soc. Am. 128(4), 2486]. Five hundred unique sentences divided into 20 lists were created. The target keywords were chosen from the most frequent lexical items occurring in naturally elicited conversations with 100 non-native talkers with varied linguistic and cultural backgrounds. The lists were equalized for syntactic structure, syllable count, high-frequency phonemes, and word frequency. Perception results revealed equal mean performance across lists for native listeners. Currently, we are in the process of collecting normative data for these materials. Results of speech-in-noise tests for a diverse group of non-native English listeners with various native language backgrounds and levels of English experience will be presented. Regression analyses will be performed to investigate how the listeners' linguistic experience, competence, and proficiency relate to their listening performance on these new sentence-recognition materials. The results of this study add to our current understanding of non-native listener-related factors that affect speech recognition in adverse listening conditions.