1. The trough effect: Can we predict tongue lowering from acoustic data alone? Yolanda Vazquez Alvarez

2. Overview 1. Background on the ‘trough effect’ 2. Aim of this experiment 3. Experimental method & results 4. Acoustic-to-articulatory mapping 5. Conclusions

3. Background – The ‘trough effect’  The ‘trough’ effect occurs in symmetrical VCV sequences and has been described as: ‘A Momentary deactivation of the tongue movement during the consonant closure’ (Bell-Berti, F. & Harris, K.; Gay, T)

4. Background – Acoustic evidence  Lindblom et al. (2002) collected direct measures of the F2 trajectories from symmetrical VCV utterances (V=/i/)

5. Background – Ultrasound evidence  Used QMUC’s data from the trough experiment  3 Annotation points corresponding to 3 different tongue contours.  2 Measurements of tongue displacement (MTD) were carried out for these 3 different contours

6. Background – Ultrasound evidence  MTDs were significantly different from each other for /iCi/ sequences (ipi (t (9) = -8.295, p< 0.010), ibi (t (9) = -9.774, p< 0.010)

7. Background – Advantages & disadvantages of both techniques  Acoustics: - Good time resolution - Doesn’t require specialised equipment to acquire the data No visualisation of the tongue  Ultrasound: - Tongue contour visualization - Physical measurement Need for frame-by-frame analysis of the tongue recording

8. Aim of this experiment Given the advantages of acoustic measurements: How confident can we be that the acoustic measurement of the tongue lowering gives us a true representation of the trough effect?

9. Experimental method Subjects5 native speakers of English, various accents Datasymmetrical VCV sequences: C=/p/,/b/ & V=/i/ Repetitions2 reps, n=20 Ultrasound analysis 3 annotation points: V 1 mid, Cmid and V 2 sym 2 distance measurements: V 1 -C and C-V 2 Acoustic analysis 4 F2 annotation points: V 1 mid, V 1 offset and F2 onset, V 2 mid 2 F2 measurements: F2V 1 -C and F2C-V 2

10. Experiment - Results Pearson correlation of V 1 -C and F2V 1 -C was significant (r (18)=.496, r2= 0.25, p<.05), predicting 25% of tongue lowering variance. Using both F2 predictors showed an increase in the correlation coefficient for V 1 -C, predicting a 43% of tongue lowering variance. Pearson correlation of C-V 2 and F2C-V 2 was not significant. Correlation of F2 values and ultrasound data (V 1 -C)

11. Experiment – Results  3 possible reasons why we couldn’t predict the rise for C-V 2 : 1. Start of the tongue rise is in the closure so F2 can’t show information about its possible movement 2. The measuring point was mainly on the release for /p/ in the ultrasound data but we used V 2 mid because otherwise we wouldn’t have sufficient F2 data 3. Ultrasound time resolution may be too poor to capture the rising of the tongue at the appropriate moment

12. Acoustic-to-articulatory mapping  Korin Richmond et al. (2003) at CSTR, Edinburgh Univ., used a multilayer perceptron (MLP) neural network to estimate articulatory trajectories  The neural network was trained on articulatory data (EMA) and acoustic data where articulatory feature vectors (x,y) were normalised to lie in the range [0.1,0.9]

13. Acoustic-to-articulatory mapping  The MLP was applied to the acoustic data from the ultrasound experiment  Despite being trained on a different speaker, the trough phenomena could be observed in the MLP estimates for the y-coordinates of tongue body movement

14. Acoustic-to-articulatory mapping Annotation times from the ultrasound measurement points were used to compare the estimated tongue positions from the MLP A tongue lowering and rising was observed in the MLP plots but no significant statistical results were obtained MLP plot for /ibi/ V 2 sym v 1 mid Cmid MLP plot for /ipi/

15. Conclusions  Acoustic information (F2) may be missing for crucial articulatory movement. It is hard to map acoustic change into articulatory change  Current ultrasound time resolution can be too poor to provide information of rapid articulatory change  However, a combined approach can help improve both techniques

16. References  Bell-Berti, F. & Harris, K. (1974). More on the motor organization of speech gestures. Haskins Laboratories: Status Report on Speech Research SR-37/38, 73-77.  Gay, T. (1975). Some electromyographic measures of coarticulation in VCV-utterances. Haskins Laboratories: Status Report on Speech Research SR-44, 137-145.  Lindblom, B., Sussman, H., Modarressi, G. & Burlingame, E. (2002). The trough effect: Implications for motor programming, Phonetica, 59, 245-262.  K. Richmond, S. King, and P. Taylor. (2003). Modelling the uncertainty in recovering articulation from acoustics. Computer Speech and Language, 17:153-172.

17. Acknowledgements Thanks go to SHS at QMUC in Edinburgh for the use of the ultrasound data from the trough experiment. Also, I would like to thank Korin Richmond at CSTR in Edinburgh for his interest and help with the processing of the acoustic data using the MLP neural network.