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Related Concept Videos

Auditory Perception01:17

Auditory Perception

649
The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the...
649
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
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Perception of Sound Waves01:01

Perception of Sound Waves

4.8K
The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same...
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Related Experiment Video

Updated: Oct 5, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Visualization of Speech Perception Analysis via Phoneme Alignment: A Pilot Study.

J Tilak Ratnanather1, Lydia C Wang1, Seung-Ho Bae1

  • 1Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.

Frontiers in Neurology
|January 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automated program for real-time phoneme accuracy visualization in speech tests for hearing loss. The tool aids clinicians and scientists by analyzing speech errors at the phoneme level.

Keywords:
F1-scorephoneme accuracyphoneme alignmentrelative information transferspeech tests

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Area of Science:

  • Audiology
  • Speech Science
  • Computational Linguistics

Background:

  • Traditional speech tests for hearing loss focus on word/sentence comprehension.
  • Phoneme-level error analysis is crucial but often manual and time-consuming.
  • A need exists for automated tools to visualize phoneme accuracy in real-time.

Purpose of the Study:

  • To develop an automated program for real-time visualization of phoneme accuracy in speech tests.
  • To analyze speech errors at the phoneme level using phonological features.
  • To provide a tool for clinicians and scientists to better understand speech perception in hearing loss.

Main Methods:

  • Utilized an open-source pronouncing dictionary for phonemic representations.
  • Modified Levenshtein Minimum Edit Distance algorithm for phoneme alignment.
  • Employed dynamic programming with phonological feature-based costs for alignment.
  • Calculated phoneme accuracy using F1-score and visualized via phonemegrams.

Main Results:

  • The program successfully processed sentence and word-level speech test data from volunteers with hearing loss.
  • Analyzed published data from cochlear implant users across various signal-to-noise ratios.
  • Demonstrated real-time visualization of phoneme accuracy and robustness across diverse datasets.
  • Confirmed the program's ability to analyze 12,400 actual and random stimulus-response pairs.

Conclusions:

  • Automated, real-time phoneme alignment from speech test data is feasible.
  • The developed program visualizes response accuracy via phonological features effectively.
  • This visualization aids in understanding speech perception deficits and improving auditory rehabilitation.