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Listeners can infer a speaker's identity from speech using probabilistic knowledge. This suggests a unified framework for understanding how social and linguistic information are processed together.

Keywords:
Computational modelingSocial perceptionSpeech perception

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Area of Science:

  • Cognitive Science
  • Psycholinguistics
  • Computational Linguistics

Background:

  • Social perceptions influence how we interpret speech.
  • Speech cues provide information about the speaker's identity.
  • Existing research highlights the connection between social and linguistic processing.

Purpose of the Study:

  • To propose a unified computational framework for social and linguistic inferences.
  • To investigate the role of probabilistic knowledge in processing social and linguistic cues.
  • To explore how listeners infer talker identity from speech signals.

Main Methods:

  • Developed a computational-level approach termed the 'ideal adapter'.
  • Utilized an ideal observer model to analyze cue distributions.
  • Employed actual speech production data to model cue covariation.

Main Results:

  • Demonstrated that talker identity can be inferred from speech cue distributions.
  • Showed the feasibility of using probabilistic knowledge for social inferences from speech.
  • Provided evidence for overlapping mechanisms in social and linguistic inference.

Conclusions:

  • A single formal framework can potentially explain both social and linguistic inferences.
  • Probabilistic knowledge is a key mechanism underlying the interaction between social and linguistic perception.
  • This research opens avenues for understanding the integration of social and acoustic information in speech perception.