Ryan Podlubny – ryan.podlubny@pg.canterbury.ac.nz
Department of Linguistics, University of Canterbury
20 Kirkwood Avenue, Upper Riccarton
Christchurch, NZ, 8041

Popular version of paper 1aNS4, “Musical mind control: Acoustic convergence to background music in speech production.”
Presented Monday morning, November 28, 2016
172nd ASA Meeting, Honolulu

People often adjust their speech to resemble that of their conversation partners – a phenomenon known as speech convergence. Broadly defined, convergence describes automatic synchronization to some external source, much like running to the beat of music playing at the gym without intentionally choosing to do so. Through a variety of studies a general trend has emerged where we find people automatically synchronizing to various aspects of their environment^1,2,3. With specific regard to language use, convergence effects have also been observed in many linguistic domains such as sentence-formation⁴, word-formation⁵, and vowel production6 (where differences in vowel production are well associated with perceived accentedness^7,8). This prevalence in linguistics raises many interesting questions about the extent to which speakers converge. This research uses a speech-in-noise paradigm to explore whether or not speakers also converge to non-linguistic signals in the environment: Specifically, will a speaker’s rhythm, pitch, or intensity (which is closely related to loudness) be influenced by fluctuations in background music such that the speech echoes specific characteristics of that background music (for example, if the tempo of background music slows down, will that influence those listening to unconsciously decrease their speech rate)?

In this experiment participants read passages aloud while hearing music through headphones. Background music was composed by the experimenter to be relatively stable with regard to pitch, tempo/rhythm, and intensity, so we could manipulate and test only one of these dimensions at a time, within each test-condition. We imposed these manipulations gradually and consistently toward a target, which can be seen in Figure 1, and would similarly return to the level at which they started after reaching that target. We played the participants music with no experimental changes in between all manipulated sessions. (Examples of what participants heard in headphones are available as sound- files 1 and 2)

Fig. 1: Using software designed for digital signal processing (analyzing and altering sound), manipulations were applied in a linear fashion (in a straight line) toward a target – this can be seen above as the blue line, which first rises and then falls. NOTE: After manipulations reach their target (the target is seen above as a dashed, vertical red line), the degree of manipulation would then return to the level at which it started in a similar linear fashion. Graphic captured while using Praat 9 to increase and then decrease the perceived loudness of the background music.

Data from 15 native speakers of New Zealand English were analyzed using statistical tests that allow effects to vary somewhat for each participant where we observed significant convergence in both the pitch and intensity conditions. Analysis of the Tempo condition, however, has not yet been conducted. Interestingly, these effects appear to differ systematically based on a person’s previous musical training. While non-musicians demonstrate the predicted effect and follow the manipulations, musicians appear to invert the effect and reliably alter aspects of their pitch and intensity in the opposite direction of the manipulation (see Figure 2). Sociolinguistic research indicates that under certain conditions speakers will emphasize characteristics of their speech to distinguish themselves socially from conversation partners or groups, as opposed to converging with them6. It seems plausible then that, given a relatively heightened ability to recognize low-level variations of sound, musicians may on some cognitive level be more aware of the variation in their sound environment, and as a result similarly resist the more typical effect. However, more work is required to better understand this phenomenon.

Fig. 2: The above plots measure pitch on the y-axis (up and down on the left edge), and indicate the portions of background music that have been manipulated on the x- axis (across the bottom). The blue lines show that speakers generally lower their pitch as an un-manipulated condition progresses. However the red lines show that when global pitch is lowered during a test-condition, such lowering is relatively more dramatic for non-musicians (left plot) and that the effect is reversed by those with musical training (right plot). NOTE: A follow-up model further accounts for the relatedness of Pitch and Intensity and shows much the same effect.

This work indicates that speakers are not only influenced by human speech partners in production, but also, to some degree, by noise within the immediate speech environment, which suggests that environmental noise may constantly be influencing certain aspects of our speech production in very specific and predictable ways. Human listeners are rather talented when it comes to recognizing subtle cues in speech¹⁰, especially compared to computers and algorithms that can’t  yet match this ability. Some language scientists argue these changes in speech occur to make understanding easier for those listening¹¹. That is why work like this is likely to resonate in both academia and the private sector, as a better understanding of how speech will change in different environments contributes to the development of more effective aids for the hearing impaired, as well as improvements to many devices used in global communications. 

Sound-file 1.
An example of what participants heard as a control condition (no experimental manipulation) in between test-conditions. 

Sound-file 2.
An example of what participants heard as a test condition (Pitch manipulation, which drops 200 cents/one full step).

References

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3.  McClintock, M. K. (1971). Menstrual synchrony and suppression. Nature, Vol 229, 244-245.
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Robots in an English Past Tense Formation Task. Journal of Language and Social Psychology, 0261927X15584682.
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9.  Boersma, P., & Weenink, D. (2014). Praat: Doing phonetics by computer (Version 5.4.04) [Computer program]. Retrieved
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