Dual learning systems in talker identification: the effects of language, accent, and feedback
Shengyue Xiong, Zhe-chen Guo, Casey L Roark, Gangyi Feng, Bharath Chandrasekaran · 2025
Read Dual learning systems in talker identification: the effects of language, accent, and feedback by Shengyue Xiong, Zhe-chen Guo, Casey L Roark, Gangyi Feng, Bharath Chandrasekaran (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12740956.txt.
Description
Shengyue Xiong and four co-authors study how listeners learn to tell talkers apart in a 2025 paper. Knowing a voice means sorting wild sound swings into one steady person. A two-system model of learning says rule-based thought starts the job, then habit-like links take over late.
Mandarin-speaking listeners trained to name talkers in their home tongue and in English, plain or with a familiar accent. Notes after each try were rich or bare, nudging one system or the other. Familiar speech sped and sharpened the naming. A drift model read the hidden choice steps behind each answer.
Catalog record
| CREATOR | Shengyue Xiong, Zhe-chen Guo, Casey L Roark, Gangyi Feng, Bharath Chandrasekaran |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 52 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |