Talker identification as a categorization problem

Casey L. Roark, Gangyi Feng, Bharath Chandrasekaran

Research output: Contribution to conferencePaperpeer-review


Learning to identify a person's voice is a key component of speech perception. In this study, we use a categorization framework to provide insights about the mechanisms supporting talker identification. Native Mandarin Chinese listeners learned to categorize sentences in three tasks with different language contexts - native Mandarin talkers speaking Mandarin, native English talkers speaking English, and native Mandarin talkers speaking English. We compared learning when listeners received fully informative or minimal feedback. Using decision bound models, we examined the strategies participants used in each of the three tasks. Regardless of language context, full feedback was initially better for learning than minimal feedback but was no different after the second block. Across tasks, participants often used strategies based on mean fundamental frequency to separate the talkers. These results demonstrate that talker identification is a categorization problem, which enables leveraging existing category learning frameworks to understand the mechanisms of this important ability.

Original languageEnglish (US)
Number of pages8
StatePublished - 2022
Event44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022 - Toronto, Canada
Duration: Jul 27 2022Jul 30 2022


Conference44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022


  • categorization
  • category learning
  • talker identification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience


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