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Related Concept Videos

Larynx01:21

Larynx

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The human larynx, often referred to as the voice box, is an intricate organ located in the neck. It serves as a pathway for air to enter the lungs during respiration and is an essential component of voice production.
Anatomy of the Larynx
The larynx consists of various components, including cartilage, muscles, and vocal cords. Its structure includes three large unpaired cartilages—the thyroid, cricoid, and epiglottis—and three smaller paired cartilages—the arytenoids,...
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Perceiving Loudness, Pitch, and Location01:21

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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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Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

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Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
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Physical Assessment of the Respiratory Tract IV: Auscultation01:28

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Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
Breath Sounds
Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.
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Suctioning the Oropharyngeal Airway01:25

Suctioning the Oropharyngeal Airway

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In preparing for oropharyngeal airway suctioning, a nurse must gather all necessary equipment, including a suction unit with tubing, a prepackaged suction kit, sterile gloves, water or saline for irrigation, a water-soluble lubricant, and additional personal protective equipment (such as a gown, mask, and goggles) to control infections.
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Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

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In assessing respiratory abnormalities, palpation and auscultation are critical tools for detecting and interpreting various pathophysiological changes. These techniques provide insight into underlying disorders by evaluating tactile sensations and sounds produced by the respiratory system.
Palpation Findings
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Related Experiment Video

Updated: Jun 7, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Leveraging laryngograph data for robust voicing detection in speech.

Yixuan Zhang1, Heming Wang1, DeLiang Wang1,2

  • 1Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA.

The Journal of the Acoustical Society of America
|November 20, 2024
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Summary

This study introduces a novel supervised voicing detection model using laryngograph data. The advanced CrossNet-based model achieves robust speech signal analysis, outperforming existing methods and generalizing to new datasets.

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Area of Science:

  • Speech processing
  • Machine learning
  • Bioacoustics

Background:

  • Accurate voiced interval detection is crucial for speech analysis, including pitch tracking.
  • Existing methods often require dataset-specific parameter tuning and exhibit limited generalization.
  • Conventional signal processing and deep learning approaches face challenges in real-world speech applications.

Purpose of the Study:

  • To develop a robust supervised voicing detection model for speech signals.
  • To improve the generalization capability of voicing detection models.
  • To provide a reliable alternative to conventional methods with less parameter tuning.

Main Methods:

  • A supervised voicing detection model adapted from the CrossNet architecture was developed.
  • The model was trained using reference voicing decisions from laryngograph datasets.
  • Pretraining strategies were investigated to enhance model generalization.

Main Results:

  • The proposed model demonstrated robust voicing detection performance.
  • It significantly outperformed established baseline methods in accuracy.
  • The model showed excellent generalization to unseen speech datasets.

Conclusions:

  • The developed supervised voicing detection model offers superior performance and generalization.
  • Leveraging laryngograph data and CrossNet architecture addresses limitations of prior methods.
  • The provided source code and datasets will aid future research in speech signal processing.