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Related Concept Videos

Hearing01:31

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When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
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Sound Waves: Interference00:53

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Sound waves can be modeled either as longitudinal waves, wherein the molecules of the medium oscillate around an equilibrium position, or as pressure waves. When two identical waves from the same source superimpose on each other, the combination of two crests or two troughs results in amplitude reinforcement known as constructive interference. If two identical waves, that are initially in phase, become out of phase because of different path lengths, the combination of crests with troughs...
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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...
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Interference: Path Lengths01:10

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Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
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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.
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Echo01:06

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The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
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Rational speech comprehension: Interaction between predictability, acoustic signal, and noise.

Marjolein Van Os1, Jutta Kray2, Vera Demberg1,3

  • 1Department of Language Science and Technology, Saarland University, Saarbrücken, Germany.

Frontiers in Psychology
|January 2, 2023
PubMed
Summary
This summary is machine-generated.

Listeners prioritize sentence context over acoustic details when speech is difficult to hear in noise. This trade-off balances top-down predictions with noisy bottom-up signals for effective speech comprehension.

Keywords:
background noisemishearingnoisy channelpredictive contextrational processingspeech comprehension

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Area of Science:

  • Cognitive Science
  • Auditory Perception
  • Speech Processing

Background:

  • Speech comprehension integrates multiple information sources.
  • Existing models often overlook sentence-level context and noise effects.
  • Prior research has limitations in controlling speech sounds and noise types.

Purpose of the Study:

  • To investigate how listeners combine contextual and acoustic information during speech comprehension in noise.
  • To examine the influence of top-down predictability, noise type, and acoustic signal characteristics.
  • To address conflicting results regarding noise types and their impact on speech intelligibility.

Main Methods:

  • An online word recognition experiment was conducted.
  • Manipulated top-down predictability, background noise type, and acoustic signal properties.
  • Created conditions varying in speech sound masking based on established principles.

Main Results:

  • Listeners increased reliance on sentence context when acoustic signals were degraded.
  • This effect persisted even with minor intelligibility differences caused by noise-speech interactions.
  • Listeners probabilistically combined contextual predictions with acoustic information.

Conclusions:

  • Speech comprehension involves a probabilistic trade-off between contextual and acoustic information.
  • The balance between top-down and bottom-up processing is influenced by specific noise-speech sound combinations.
  • Listeners adapt their information weighting based on signal clarity and noise characteristics.