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.
|Preprint (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.
Unless otherwise noted, software available on this page is provided under the GNU General Public License v3.0. The GNU GPL is the most widely used free software license and has a strong copyleft requirement. When distributing derived works, the source code of the work must be made available under the same license. Redistribution of this code must also include (1) a copy of the enclosed license and copyright notice, (2) state what changes, if any, have been made to the code, (3) provide attribution to the authors of the code, in this case the SPIN-Scorcerer Team, and (4) remain under the same GNU GPL v3.0 license.