This page provides an index of SPIN-Scorcerer code, authored by Meredith Riggs in the SoundBrain Lab at the University of Texas (Austin). The software uses common NLP tools (e.g., Python’s Pattern module) to analyze human-transcribed responses to speech in noise perception tasks. Please contact the development team for questions or assistance.
Benjamin Zinszer, Meredith Riggs, Rachel Reetzke, & Bharath Chandrasekaran. (2019)
|Article in JASA-EL||Stats Supplement||Code & Data Repository|
Abstract. Speech perception in noise requires both bottom-up sampling of the stimulus and top-down reconstruction of the masked signal from a language model. Previous studies have provided mixed evidence about the exact role that linguistic knowledge plays in native and non-native listeners’ perception of masked speech. This paper describes an analysis of whole utterance, content word, and morphosyntactic error patterns to test the prediction that non-native listeners are uniquely affected by energetic and informational masks because of limited information at multiple linguistic levels. The results reveal a consistent disadvantage for non-native listeners at all three levels in challenging listening environments.
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