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Chaos Behavior Analysis of Alaryngeal Voices Including Esophageal and Tracheoesophageal Voices
Boquan Liu1, Fan Zhang2, Ling Chen2
1School of Humanities, Shanghai Jiao Tong University, Shanghai, China.
Hypothesis/Objectives:
This study's objective was to develop a method to 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 (CB) and the difference between SE and TE voices.
Study Design:
A total of 74 voice recordings including 34 normal and 40 alaryngeal (26 SE and 14 TE) were used in the study. Voice samples were analyzed to distinguish alaryngeal voices from normal voices and to investigate different chaotic characteristics of SE and TE speech.
Methods:
A chaotic distribution detection-based method was used to investigate the CB of alaryngeal voices. This CB was used to detect the difference between SE and TE voice types. Quantification of the 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 CB 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 acoustic information when monitoring the efficacy of treatment for patients undergoing total laryngectomies.
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