When assessing a second language speaker’s nativelikeness or accentedness, researchers often employ holistic judgments (Abrahamsson & Hyltenstam 2009, Ioup et al. 1994) or auditory analysis of specific segments (Rampton 2013). Acoustic analysis, which can help quantify minute details, can be quite time-consuming when large corpora are involved. This research note describes the Accents of Non-Native English (ANNE) learner corpus which employs the open-source Language Brain and Behaviour-Corpus Annotation Tool (LaBB-CAT; Fromont & Hay 2012) that allows researchers to automatically extract timing information about segments in the corpus and process them with Praat (Boersma & Weenink 2009), facilitating large-scale acoustic analysis.
2024. The complexities of linguistic discrimination. Philosophical Psychology► pp. 1 ff.
Gnevsheva, Ksenia, Simon Gonzalez & Robert Fromont
2020. Australian English Bilingual Corpus: Automatic forced-alignment accuracy in Russian and English. Australian Journal of Linguistics 40:2 ► pp. 182 ff.
Gnevsheva, Ksenia
2016. Beyond the language: listener comments on extra-linguistic cues in perception tasks. Language Awareness 25:4 ► pp. 257 ff.
Gnevsheva, Ksenia
2018. Variation in foreign accent identification. Journal of Multilingual and Multicultural Development 39:8 ► pp. 688 ff.
Gnevsheva, Ksenia
2018. The expectation mismatch effect in accentedness perception of Asian and Caucasian non-native speakers of English. Linguistics 56:3 ► pp. 581 ff.
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