SPIN-Scorcerer code repository

SPIN-Scorcerer Logo, a sorcerer's or wizard's pointed blue hat with yellow stars on itThis 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.

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Error patterns of native and non-native listeners’ perception of speech in noise

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.

Impact of depression on speech perception in noise

Zilong Xie, Benjamin Zinszer, Meredith Riggs, Christopher G. Beevers, Bharath Chandrasekaran. (2019)

Article in PLOS ONE Data Repository

Abstract. Effective speech communication is critical to everyday quality of life and social well-being. In addition to the well-studied deficits in cognitive and motor function, depression also impacts communication. Here, we examined speech perception in individuals who were clinically diagnosed with major depressive disorder (MDD) relative to neurotypical controls. Forty-two normal-hearing (NH) individuals with MDD and 41 NH neurotypical controls performed sentence recognition tasks across three conditions with maskers varying in the extent of linguistic content (high, low, and none): 1-talker masker (1T), reversed 1-talker masker (1T_tr), and speech-shaped noise (SSN). Individuals with MDD, relative to neurotypical controls, demonstrated lower recognition accuracy in the 1T condition but not in the 1T_tr or SSN condition. To examine the nature of the listening condition-specific speech perception deficit, we analyzed speech recognition errors. Errors as a result of interference from masker sentences were higher for individuals with MDD (vs. neurotypical controls) in the 1T condition. This depression-related listening condition-specific pattern in recognition errors was not observed for other error types. We posit that this depression-related listening condition-specific deficit in speech perception may be related to heightened distractibility due to linguistic interference from background talkers.

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.