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Vocal microtremor in normophonic and mildly dysphonic speakers Jean Schoentgen Université Libre Bruxelles Brussels - Belgium.

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Presentation on theme: "Vocal microtremor in normophonic and mildly dysphonic speakers Jean Schoentgen Université Libre Bruxelles Brussels - Belgium."— Presentation transcript:

1 Vocal microtremor in normophonic and mildly dysphonic speakers Jean Schoentgen Université Libre Bruxelles Brussels - Belgium

2 Vocal microtremor (definition) Modulation of the phonatory frequency Distinct from pathological vocal tremor 1 - 15 Hz (Titze, 1995, Sataloff, 1997) Two features : modulation level and modulation frequency

3 Vocal microtremor (examples)

4 Motivation ? Tremor data are scarce Vocal jitter & microtremor are base-line phenomena Measurement of vocal tremor frequency via the cycle length time series Test predictions of a simulation model of jitter and tremor (Schoentgen, 2001)

5 Experiment I : Objectives ? Recording data (tremor level & frequency) Differences between vowel timbres ? Differences between male & female speakers ? Differences between normophonic & mildly dysphonic speakers ?

6 Corpora Sustained vowels [a], [i], [u] 22 males, 16 females (normophonic) 16 males, 28 females (dysphonic) Voice type : monocycle periodic Register : modal No register or type breaks, or voice arrests No cycle length outliers No excessive additive noise or jitter No pathological vocal tremor

7 Method (tremor frequency) 1.Estimation of the average cycle length 2.Upsampling (160 kHz) and low-pass filtering of the speech signal 3.Extraction of the vocal cycle length time series via peak picking 4.Removal of frequency drift or glissando 5.Calculation of the magnitude spectrum of the time series 6.Search for the statistically significant spectral peaks 7.Tremor frequency = weighted average of spectral peak positions

8 Examples of spectra

9 Method (tremor level) 1.Upsampling (160kHz) and low-pass filtering of the speech signal 2.Extraction of the vocal cycle length time series via peak picking 3.Removal of frequency drift or glissando 4.Smoothing of the time series to decrease jitter 5.Tremor level -> standard deviation of the smoothed cycle length perturbations (divided by average cycle length)

10 Results (1) No statistically significant differences for the modulation frequency (Hz) and modulation level (%) between : Male & female speakers Normophonic & mildly dysphonic speakers Vowel timbres

11 Results (2) FeatureInter-quartile interval Modulation level0.4 % - 1.3 % Modulation frequency2.2 - 4.5 Hz

12 Results (3) Dissimilarities between modulation data reported by different studies are due to different cutoff frequencies below which spectral peaks are considered not to contribute to vocal microtremor

13 Experiment II : Objective Compare the size of vocal cycle length perturbations owing to jitter and frequency tremor

14 Corpus & Method 22 male and 16 female speakers sustained [a], [i] and [u]. oUpsampling (160kHz) and low-pass filtering of the speech signal oExtraction of the vocal cycle length time series via signal zero-crossings oRemoval of frequency drift or glissando

15 Linear auto-regressive analysis of the cycle length time series (e.g. Schoentgen, 1995) Present perturbation = weighted sum of past perturbations + de-correlated noise De-correlated noise -> vocal jitter Weighted sum -> vocal tremor (by default) Calculate sample standard deviation for each (& divide by average cycle length)

16 Example

17 Results (1) : [a] (inter-quartile ranges) malefemale Relative raw perturbations0.6 - 1.1%0.6 - 1.4% Modulation level0.5 - 1.0%0.6 - 1.2% Relative vocal jitter0.3 - 0.5%0.3 - 0.4%

18 Results (2) Vocal jitter (%) < vocal tremor (%) (statistically significant) Moderate significant correlation between vocal jitter & tremor (in %) No significant tremor differences between vowel timbres No significant tremor differences between speaker genders

19 Conclusion Vocal frequency (micro)tremor data can be obtained via the cycle length time series This may be generalized to pathological tremor data, but an additional stage may be required which is the re-sampling of the cycle length time series at equal intervals


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