School of Humanities, Shanghai Jiao Tong University, Shanghai, China.
Otorhinolaryngology Department of the Eye, ENT Hospital affiliated with Fudan University, Shanghai, China.
Folia Phoniatr Logop. 2022;74(6):431-440. doi: 10.1159/000521222. Epub 2022 Jan 20.
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.
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.
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.
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).
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.
假设/目的：本研究的目的是开发一种评估喉切除术后语音混沌特征的方法。该方法能够区分正常语音和喉切除术后语音，包括食管语音（SE）和气管食管语音（TE）。先前的研究表明，喉切除术后语音由于信号的非周期性而表现出混沌特征。该方法将用于未来对混沌行为（CB）和 SE 与 TE 语音之间的差异进行定量分析。
本研究共使用了 74 段语音录音，包括 34 段正常语音和 40 段喉切除术后语音（26 段 SE 和 14 段 TE）。对语音样本进行分析，以区分喉切除术后语音和正常语音，并研究 SE 和 TE 语音的不同混沌特征。
使用基于混沌分布检测的方法来研究喉切除术后语音的 CB。该 CB 用于检测 SE 和 TE 语音类型之间的差异。对 CB 参数进行量化。使用统计分析比较 SE 和 TE 语音的 CB 分析结果。
统计分析表明，CB 能够有效地区分所有正常和喉切除术后语音类型（p < 0.01）。随后的多类接收器操作特征（ROC）分析表明，CB（曲线下面积）相对于关联维数（D2）具有最高的分类准确性。
CB 度量值有望成为一种准确、有用的指标，用于客观地区分所有正常和喉切除术后语音、SE 和 TE 语音类型。CB 计算结果符合预期，因为 SE 语音的 CB 明显高于 TE 语音，与之前的方法相比有了显著的改进，成为首个 SE 和 TE 分类方法。该度量值可帮助临床医生在监测全喉切除术后患者治疗效果时获得额外的声学信息。
