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Automatic Processing and Automatic Social Behavior01:28

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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Related Experiment Video

Updated: Feb 28, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Automated cleft speech evaluation using speech recognition.

Megan Vucovich1, Rami R Hallac2, Alex A Kane2

  • 1Department of Plastic Surgery, UT Southwestern Medical Center (Chairman: Dr. Jeffrey Kenkel), 1801 Inwood Rd, Dallas, TX 75390, United States.

Journal of Cranio-Maxillo-Facial Surgery : Official Publication of the European Association for Cranio-Maxillo-Facial Surgery
|June 13, 2017
PubMed
Summary
This summary is machine-generated.

A new computer learning system automatically evaluates cleft speech for resonance and articulation errors. This automated cleft speech evaluator offers unbiased assessment, addressing listener shortages and aiding global collaboration.

Keywords:
Automated speech evaluatorCleft palateCleft speechSpeech recognitionVelopharyngeal dysfunction

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Area of Science:

  • Speech-Language Pathology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Perceptual evaluation is the gold standard for cleft speech assessment but is subjective and resource-intensive.
  • Global shortages of trained listeners and the labor-intensive nature of manual evaluation hinder consistent cleft speech assessment.
  • Existing speech recognition systems often overlook critical voice characteristics and speech errors relevant to cleft speech.

Purpose of the Study:

  • To develop and validate an automated computer learning system for evaluating cleft speech.
  • To interpret resonance and articulatory errors in cleft speech, distinguishing between normal speech, velopharyngeal dysfunction, and articulatory disorders.
  • To provide an unbiased, efficient, and accessible tool for cleft speech evaluation.

Main Methods:

  • A computer learning system was developed to analyze resonance and articulatory errors in cleft speech.
  • Speech samples from 60 patients were used to train the evaluator, with an additional 13 patients for testing.
  • The system was trained to differentiate normal speech, velopharyngeal dysfunction, and articulatory errors, focusing on voice characteristics and speech errors.

Main Results:

  • The automated cleft speech evaluator achieved 77% accuracy on its best sentence and a median of 65% across all sentences.
  • The inter-speech pathologist agreement rate was 79%, providing a benchmark for the automated system's performance.
  • The system successfully targeted voice characteristics and speech errors crucial for cleft speech evaluation.

Conclusions:

  • The developed automated cleft speech evaluator shows promise for objective and accessible cleft speech assessment.
  • This tool can support global cleft speech evaluation efforts, especially in areas with limited access to speech pathologists.
  • Further increases in training data are anticipated to enhance the system's accuracy, potentially matching human listener performance.