AIMC Topic: Vocal Cords

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Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.

PloS one Oct 18, 2021
Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate th...
Language Learning Deep Learning Support Vector Machine Least-Squares Analysis Speech Perception Speech Acoustics Vocal Cords Humans
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Deep Learning Application for Vocal Fold Disease Prediction Through Voice Recognition: Preliminary Development Study.

Journal of medical Internet research Jun 8, 2021
BACKGROUND: Dysphonia influences the quality of life by interfering with communication. However, a laryngoscopic examination is expensive and not readily accessible in primary care units. Experienced laryngologists are required to achieve an accurate...
Quality of Life Deep Learning Vocal Cords Voice Recognition Humans Artificial Intelligence
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A Deep Learning Enhanced Novel Software Tool for Laryngeal Dynamics Analysis.

Journal of speech, language, and hearing research : JSLHR May 17, 2021
Purpose High-speed videoendoscopy (HSV) is an emerging, but barely used, endoscopy technique in the clinic to assess and diagnose voice disorders because of the lack of dedicated software to analyze the data. HSV allows to quantify the vocal fold osc...
Vocal Cords Laryngoscopy Vibration Phonation Humans Software Deep Learning Video Recording Larynx Glottis
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Comparison of Convolutional Neural Network Models for Determination of Vocal Fold Normality in Laryngoscopic Images.

Journal of voice : official journal of the Voice Foundation Aug 30, 2020
OBJECTIVES: Deep learning using convolutional neural networks (CNNs) is widely used in medical imaging research. This study was performed to investigate if vocal fold normality in laryngoscopic images can be determined by CNN-based deep learning and ...
Deep Learning Vocal Cords Laryngoscopy Humans Neural Networks, Computer Sensitivity and Specificity
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An Open-Source Computer Vision Tool for Automated Vocal Fold Tracking From Videoendoscopy.

The Laryngoscope May 1, 2020
OBJECTIVES: Contemporary clinical assessment of vocal fold adduction and abduction is qualitative and subjective. Herein is described a novel computer vision tool for automated quantitative tracking of vocal fold motion from videolaryngoscopy. The po...
Computers Female Aged Aged, 80 and over Humans Software Deep Learning Video Recording Vocal Cord Paralysis Vocal Cords Adult Male Middle Aged Case-Control Studies Laryngoscopy
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Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network.

PloS one Feb 10, 2020
The objective investigation of the dynamic properties of vocal fold vibrations demands the recording and further quantitative analysis of laryngeal high-speed video (HSV). Quantification of the vocal fold vibration patterns requires as a first step t...
Glottis Image Interpretation, Computer-Assisted Endoscopy Neural Networks, Computer Humans Time Factors Databases as Topic Algorithms Anatomic Landmarks Vocal Cords Female Larynx Video Recording Adult
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A Convolutional Neural Network for Real Time Classification, Identification, and Labelling of Vocal Cord and Tracheal Using Laryngoscopy and Bronchoscopy Video.

Journal of medical systems Jan 2, 2020
BACKGROUND: The use of artificial intelligence, including machine learning, is increasing in medicine. Use of machine learning is rising in the prediction of patient outcomes. Machine learning may also be able to enhance and augment anesthesia clinic...
Vocal Cords Laryngoscopy Sensitivity and Specificity Male Infant Adolescent Child, Preschool Child Bronchoscopy Trachea Machine Learning Algorithms Humans Female Neural Networks, Computer Young Adult
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A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation.

International journal of computer assisted radiology and surgery Jan 16, 2019
PURPOSE: Automated segmentation of anatomical structures in medical image analysis is a prerequisite for autonomous diagnosis as well as various computer- and robot-aided interventions. Recent methods based on deep convolutional neural networks (CNN)...
Humans Neural Networks, Computer Machine Learning Endoscopy Image Processing, Computer-Assisted Semantics Vocal Cords Laryngoscopy Larynx
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Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

Journal of voice : official journal of the Voice Foundation Mar 19, 2018
OBJECTIVES: Computerized detection of voice disorders has attracted considerable academic and clinical interest in the hope of providing an effective screening method for voice diseases before endoscopic confirmation. This study proposes a deep-learn...
Voice Quality Dysphonia Sound Spectrography Predictive Value of Tests Female Speech Acoustics Diagnosis, Computer-Assisted Signal Processing, Computer-Assisted Vocal Cords Reproducibility of Results Young Adult Deep Learning Acoustics Humans Speech Production Measurement Aged Adult Pilot Projects Retrospective Studies Aged, 80 and over Male Support Vector Machine Middle Aged
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Hierarchical Classification and System Combination for Automatically Identifying Physiological and Neuromuscular Laryngeal Pathologies.

Journal of voice : official journal of the Voice Foundation Oct 12, 2016
OBJECTIVES: Speech signal processing techniques have provided several contributions to pathologic voice identification, in which healthy and unhealthy voice samples are evaluated. A less common approach is to identify laryngeal pathologies, for which...
Speech Production Measurement Predictive Value of Tests Voice Quality Signal Processing, Computer-Assisted Male Speech Acoustics Humans Pattern Recognition, Automated Case-Control Studies Databases, Factual Vocal Cord Paralysis Edema Diagnosis, Computer-Assisted Female Vocal Cords Support Vector Machine Voice Disorders
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