2. Pitch and Amplitude Perturbation (Jitter and Shimmer) Basic idea: Phonated speech is called quasiperiodic, with quasi being Latin for “sort of” or “more-or-less. Voiced speech is never perfectly periodic. Random cycle-to-cycle variability in f 0 (or, equivalently, t 0 – the fundamental period) is called pitch perturbation or jitter. Random cycle-to-cycle variability in the amplitude of glottal pulses is called amplitude perturbation or shimmer.

3. Basic idea: Phonated speech is called quasiperiodic, with quasi being Latin for “sort of” or “more-or-less. Voiced speech is never perfectly periodic. Random cycle-to-cycle variability in f 0 (or, equivalently, t 0 – the fundamental period) is called pitch perturbation or jitter. (Also – and more properly – known as fundamental frequency perturbation, although that term is seldom used.) Random cycle-to-cycle variability in the amplitude of glottal pulses is called amplitude perturbation or shimmer.

4. The jitter concept is quite simple. This signal looks perfectly periodic, but it isn’t. There are small, more-or-less random differences in the fund. period from one cycle to the next.

5. How Jitter is Measured: Mean Jitter Mean Jitter = sum of (abs) period diffs / number of diffs = 0.2 + 0.1 + 0.0 / 3 = 0.3 / 3 = 0.1 ms In English: MeanJ = SumOfAbsDiffs / ndiffs

6. Percent Jitter It is far more common to represent jitter as a percentage of the average period: Percent Jitter = MeanJ/MeanPeriod x 100 Our example from the previous slide: MeanJ = 0.1 ms; MeanPeriod = 8 ms Percent Jitter = 0.1/8 * 100 = 0.125/8.0 x 100 = 1.25%

7. What We Know About Jitter 1.Jitter values in non-dysphonic voices are quite small – about 0.5%. 2.There is a good deal of research to show that jitter values in dysphonic voices are significantly larger. 3.Jitter is thought to be associated with a sensation of roughness in the voice. 4.For that reason, it has been proposed as an objective correlate of either roughness or overall dysphonia.

8. Shimmer: More-or-less random cycle-to-cycle variation in voice amplitude (vocal intensity) rather than f 0.

9. What We Know About Shimmer 1.Shimmer values in non-dysphonic voices are quite small – usually less ~0.7 dB. 2.As with jitter, shimmer values in dysphonic voices are significantly larger. 3.Shimmer is thought by some to be associated with a sensation of roughness in the voice – but this is not settled. You’ll be able to decide this on your own in a few minutes.

10. Synthetic Continuum Varying in Jitter 0.0%2.0% 0.2%2.5% 0.4%3.0% 0.6%4.0% 0.8%5.0% 1.0%6.0% 1.5%

11. Shimmer calculation: There are calculations for shimmer that are analogous to the ones we saw earlier for jitter – the average absolute difference in amplitude between adjacent periods. (There is no percent shimmer, for reasons we won’t worry about.)

12. Synthetic Continuum Varying in Shimmer 0.00 dB1.60 dB 0.20 dB1.80 dB 0.40 dB2.00 dB 0.60 dB2.25 dB 0.80 dB2.50 dB 1.00 dB2.75 dB 1.20 dB3.00 dB 1.40 dB

13. Jitter Versus Shimmer Do pitch perturbation and amplitude perturbation produce the same kinds of sound qualities? Jitter (6%)Shimmer (3 dB) Judge for yourself, but these sound quite different to my ear. Jitter continuum: Clear to very rough, Shimmer continuum: Clear to unnaturally crackly, somewhat like static.

14. One More Problem: Measurement Accuracy Jitter and shimmer measurements require that the starting and ending times of every pitch pulse be measured with a lot of precision. This is waaaay too time consuming to do by hand, so a computer algorithm is needed. Measuring the t 0 is not too hard for highly periodic voices. But what voices are the most interesting? Highly periodic voices or dysphonic voices that show imperfect periodicity?

15. This is the problem: The voices that are most interesting are exactly the ones that are the hardest to measure. Also, the quantities being measured (e.g., jitter) are quite small – only a few percent even in dysphonic voices – so even a few errors can make a big difference. There are commercial systems available for measuring perturbation. They’re well designed, but they will sometimes make mistakes. This doesn’t mean that you shouldn’t use them, but when the computer gives you a measurement that doesn’t agree with what your ear tells you, your ear could easily be right.