Literature DB >> 35051938

Chaos Behavior Analysis of Alaryngeal Voices Including Esophageal (SE) and Tracheoesophageal (TE) Voices.

Boquan Liu, Fan Zhang, Ling Chen, Matthew A Silverman, Hengxin Liu, Dehui Fu, Yongwang Huang, Jing Pan, Jack J Jiang.   

Abstract

Hypothesis/Objectives This study's objective was to develop a method to the evaluate the chaotic characteristic of alaryngeal speech. The proposed method will be capable of distinguishing between normal and alaryngeal voices, including esophageal (SE) and tracheoesophageal (TE) voices. It has been previously shown that alaryngeal voices exhibit chaotic characteristics due to the aperiodicity of their signals. The proposed method will be applied for future use to quantify both chaos behavior and the difference between SE and TE voices. Study Design A total of 74 voice recordings including 34 normal and 40 alaryngeal (26 esophageal (SE) and 14 tracheoesophageal (TE)) were used in the study. Voice samples were analyzed to distinguish alaryngeal voices from normal voices and investigate different chaotic characteristics of SE and TE speech. Methods A chaotic distribution detection-based method was used to investigate the chaos behavior of alaryngeal voices. This chaos behavior was used to detect the difference between SE and TE voice types. Quantification of the chaos behavior (CB) parameter was performed. Statistical analyses were used to compare the results of the CB analysis for both the SE and TE voices. Results Statistical analysis revealed that CB effectively differentiated between all normal and alaryngeal voice types (P<0.01). Subsequent multiclass receiver operating characteristic (ROC) analysis demonstrated that CB (area under the curve) possessed the greatest classification accuracy relative to Correlation dimension (D2). Conclusions The CB metric shows strong promise as an accurate, useful metric for objective differentiation between all normal and alaryngaeal, SE and TE voice types. The CB calculations showed expected results, as SE voices have significantly more chaos behavior than TE voices, constituting substantial improvement over previous methods and becoming the first SE and TE classification method. This metric can help clinicians obtain additional acoustical information when monitoring the efficacy of treatment for patients undergoing total laryngectomies. S. Karger AG, Basel.

Entities:  

Year:  2022        PMID: 35051938      PMCID: PMC9296702          DOI: 10.1159/000521222

Source DB:  PubMed          Journal:  Folia Phoniatr Logop        ISSN: 1021-7762            Impact factor:   1.391


  30 in total

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Authors:  W A Ainsworth; W Singh
Journal:  Folia Phoniatr (Basel)       Date:  1992

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Authors:  Corina J van As-Brooks; Florien J Koopmans-van Beinum; Louis C W Pols; Frans J M Hilgers
Journal:  J Voice       Date:  2005-09-26       Impact factor: 2.009

3.  Acoustic analysis of aperiodic voice: perturbation and nonlinear dynamic properties in esophageal phonation.

Authors:  Julia K Maccallum; Li Cai; Liang Zhou; Yu Zhang; Jack J Jiang
Journal:  J Voice       Date:  2008-04-14       Impact factor: 2.009

4.  The Relationship Between Acoustic Signal Typing and Perceptual Evaluation of Tracheoesophageal Voice Quality for Sustained Vowels.

Authors:  Renee P Clapham; Corina J van As-Brooks; Rob J J H van Son; Frans J M Hilgers; Michiel W M van den Brekel
Journal:  J Voice       Date:  2015-03-17       Impact factor: 2.009

5.  The use of the Lombard Effect in Improving Alaryngeal Speech.

Authors:  Manwa L Ng
Journal:  J Voice       Date:  2019-07-23       Impact factor: 2.009

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Authors:  F Debruyne; P Delaere; J Wouters; P Uwents
Journal:  J Laryngol Otol       Date:  1994-04       Impact factor: 1.469

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Authors:  L Max; W Steurs; W de Bruyn
Journal:  Laryngoscope       Date:  1996-01       Impact factor: 3.325

8.  Acoustical analysis and perceptual evaluation of tracheoesophageal prosthetic voice.

Authors:  C J van As; F J Hilgers; I M Verdonck-de Leeuw; F Koopmans-van Beinum
Journal:  J Voice       Date:  1998-06       Impact factor: 2.009

9.  Nonlinear dynamic analysis of disordered voice: the relationship between the correlation dimension (D2) and pre-/post-treatment change in perceived dysphonia severity.

Authors:  Shaheen N Awan; Nelson Roy; Jack J Jiang
Journal:  J Voice       Date:  2009-06-07       Impact factor: 2.009

10.  Tracheostomy cannulas and voice prosthesis.

Authors:  Burkhard Kramp; Steffen Dommerich
Journal:  GMS Curr Top Otorhinolaryngol Head Neck Surg       Date:  2011-03-10
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