Lin Li1, Yu Zhang2, William Calawerts3, Jack J Jiang3. 1. Department of Applied Marine Physics & Engineering, College of Oceanography and Environmental Science, Xiamen University, Xiamen, Fujian, People's Republic of China. 2. Department of Applied Marine Physics & Engineering, Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University, Xiamen, Fujian, People's Republic of China; Department of Applied Marine Physics & Engineering, State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, Fujian, People's Republic of China. Electronic address: yuzhang@xmu.edu.cn. 3. Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
Abstract
OBJECTIVES: There are four types of signals that are typical representations of vocal fold vibratory patterns. Type 1 signals are nearly periodic, type 2 signals contain subharmonic properties, type 3 signals are chaotic, and type 4 signals are characterized as white noise. High-speed imaging allows detailed observation of these vocal fold vibratory patterns. Therefore, high-speed imaging can explore the vibratory mechanism behind each of the four types of signals. METHODS: The glottal area time series of the four types of vocal fold vibrations were calculated from high-speed images of 10 excised canine larynges. Nonlinear dynamic parameters of correlation dimension (D2) and Kolmogorov entropy (K2) were used to quantify the characteristics of the glottal areas and acoustical signals for each voice signal type. RESULTS: The correlation dimension and Kolmogorov entropy of the glottal areas and acoustical signals for type 1, 2, and 3 voice signals were consistent with the results of previous studies. Interestingly, there was a difference between the glottal area and acoustical signals of type 4 voice signals (P < 0.001). Both the correlation dimension and Kolmogorov entropy of the type 4 glottal area were close to 0. In contrast, the type 4 acoustical signals had an infinite correlation dimension and a Kolmogorov entropy that was close to 1. CONCLUSIONS: Turbulence in the vocal tract creates high-frequency breathiness, causing noise in the acoustical signal of type 4 voice, proving that the acoustical signal does not represent the motion mechanism behind type 4 voice. The results of this study demonstrate that high-speed imaging can provide a more accurate representation of the type 4 vocal fold vibratory pattern, and a more effective method to explore the mechanism of type 4 signals. Copyright Â
OBJECTIVES: There are four types of signals that are typical representations of vocal fold vibratory patterns. Type 1 signals are nearly periodic, type 2 signals contain subharmonic properties, type 3 signals are chaotic, and type 4 signals are characterized as white noise. High-speed imaging allows detailed observation of these vocal fold vibratory patterns. Therefore, high-speed imaging can explore the vibratory mechanism behind each of the four types of signals. METHODS: The glottal area time series of the four types of vocal fold vibrations were calculated from high-speed images of 10 excised canine larynges. Nonlinear dynamic parameters of correlation dimension (D2) and Kolmogorov entropy (K2) were used to quantify the characteristics of the glottal areas and acoustical signals for each voice signal type. RESULTS: The correlation dimension and Kolmogorov entropy of the glottal areas and acoustical signals for type 1, 2, and 3 voice signals were consistent with the results of previous studies. Interestingly, there was a difference between the glottal area and acoustical signals of type 4 voice signals (P < 0.001). Both the correlation dimension and Kolmogorov entropy of the type 4 glottal area were close to 0. In contrast, the type 4 acoustical signals had an infinite correlation dimension and a Kolmogorov entropy that was close to 1. CONCLUSIONS: Turbulence in the vocal tract creates high-frequency breathiness, causing noise in the acoustical signal of type 4 voice, proving that the acoustical signal does not represent the motion mechanism behind type 4 voice. The results of this study demonstrate that high-speed imaging can provide a more accurate representation of the type 4 vocal fold vibratory pattern, and a more effective method to explore the mechanism of type 4 signals. Copyright Â