Matthias Heyne, myself, and Jalal Al-Tamimi recently published Native language influence on brass instrument performance: An application of generalized additive mixed models (GAMMs) to midsagittal ultrasound images of the tongue. The paper contains the bulk of the results form Matthias’ PhD Dissertation. The study is huge, with ultrasound tongue recordings of 10 New Zealand English (NZE) and 10 Tongan trombone players. There are 12,256 individual tongue contours of vowel tokens (7,834 for NZE, 4,422 for Tongan) and 7,428 individual tongue contours of sustained note production (3,715 for NZE, 3,713 for Tongan).

The results show that native language influences tongue position during Trombone note production. This includes tongue position and note variability. The results also support Dispersion Theory (Liljencrants and Lindblom 1972; Lindblom, 1986; Al-Tamimi and Ferragne, 2005) in that vowel production is more variable in Tongan, which has few vowels, then in NZE, which has many.

The results also show that note production at the back of the tongue maps to low-back vowel production (schwa and ‘lot’ for NZE, /o/ and /u/ for schwa). These two result sets support an analysis of local optimization with semi-independent tongue regions (Ganesh et al., 2010, Loeb, 2012).

The results do not, however, support the traditional brass pedagogy hypothesis that higher notes are played with a closer (higher) tongue position. However, Matthias is currently working with MRI data that *does* support the brass pedagogy hypothesis, and that we might not have seen this because of the ultrasound transducer stabilization system needed to keep the ultrasound probe aligned to the participant’s head.

Liljencrants, Johan, and Björn Lindblom. 1972. “Numerical Simulation of Vowel Quality Systems: The Role of Perceptual Contrast.” Language, 839–62.

Lindblom, Björn. 1963. Spectrographic study of vowel reduction. The Journal of the Acoustical Society of America 35(11): 1773–1781.

Al-Tamimi, J., and Ferragne, E. 2005. “Does vowel space size depend on language vowel inventories? Evidence from two Arabic dialects and French,” in Proceedings of the Ninth European Conference on Speech Communication and Technology, Lisbon, 2465–2468.

Ganesh, Gowrishankar, Masahiko Haruno, Mitsuo Kawato, and Etienne Burdet. 2010. “Motor Memory and Local Minimization of Error and Effort, Not Global Optimization, Determine Motor Behavior.” Journal of Neurophysiology 104 (1): 382–90.

Loeb, Gerald E. 2012. “Optimal Isn’t Good Enough.” Biological Cybernetics 106 (11–12): 757–65.

Myself, Doreen Hansmann, and Catherine Theys just published our article on “Tri-modal Speech: Audio-visual-tactile Integration in Speech Perception” in the Journal of the Acoustical Society of America. This paper was also presented as a poster at the American Speech-Language-Hearing Association (ASHA) Annual Convention in Orlando, Florida, November 21-22, 2019, winning a meritorious poster award.

TL-DR; People use auditory, visual, and tactile speech information to accurately identify syllables in noise. Auditory speech information is the most important, then visual information, and lastly aero-tactile information – but we can use them all at once.

Abstract

Speech perception is a multi-sensory experience. Visual information enhances (Sumby and Pollack, 1954) and interferes (McGurk and MacDonald, 1976) with speech perception. Similarly, tactile information, transmitted by puffs of air arriving at the skin and aligned with speech audio, alters (Gick and Derrick, 2009) auditory speech perception in noise. It has also been shown that aero-tactile information influences visual speech perception when an auditory signal is absent (Derrick, Bicevskis, and Gick, 2019a). However, researchers have not yet identified the combined influence of aero-tactile, visual, and auditory information on speech perception. The effects of matching and mismatching visual and tactile speech on two-way forced-choice auditory syllable-in-noise classification tasks were tested. The results showed that both visual and tactile information altered the signal-to-noise threshold for accurate identification of auditory signals. Similar to previous studies, the visual component has a strong influence on auditory syllable-in-noise identification, as evidenced by a 28.04 dB improvement in SNR between matching and mismatching visual stimulus presentations. In comparison, the tactile component had a small influence resulting in a 1.58 dB SNR match-mismatch range. The effects of both the audio and tactile information were shown to be additive.

Derrick, D., Bicevskis, K., and Gick, B. (2019a). “Visual-tactile speech perception and the autism quotient,” Frontiers in Communication – Language Sciences 3(61), 1–11, doi: http://dx.doi.org/10.3389/fcomm.2018.00061

Gick, B., and Derrick, D. (2009). “Aero-tactile integration in speech perception,” Nature 462, 502–504, doi: https://doi.org/10.1038/nature08572.

McGurk, H., and MacDonald, J. (1976). “Hearing lips and seeing voices,” Nature 264, 746–748, doi: http://dx.doi.org/https://doi.org/10.1038/264746a0