Musical mind control: Human speech takes on characteristics of background music
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-formation 5, 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 speech 10, 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 listening 11. 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).
1. Hill, A. R., Adams, J. M., Parker, B. E., & Rochester, D. F. (1988). Short-term entrainment of ventilation to the walking cycle in humans. Journal of Applied Physiology, 65(2), 570-578.
2. Will, U., & Berg, E. (2007). Brain wave synchronization and entrainment to periodic acoustic stimuli. Neuroscience letters, 424(1), 55-60.
3. McClintock, M. K. (1971). Menstrual synchrony and suppression. Nature, Vol 229, 244-245.
4. Branigan, H. P., Pickering, M. J., McLean, J. F., & Cleland, A. A. (2007). Syntactic alignment and participant role in dialogue. Cognition, 104(2), 163-197.
5. Beckner, C., Rácz, P., Hay, J., Brandstetter, J., & Bartneck, C. (2015). Participants Conform to Humans but Not to Humanoid
Robots in an English Past Tense Formation Task. Journal of Language and Social Psychology, 0261927X15584682.
Retreived from: http://jls.sagepub.com.ezproxy.canterbury.ac.nz/content/early/2015/05/06/0261927X15584682.
6. Babel, M. (2012). Evidence for phonetic and social selectivity in spontaneous phonetic imitation. Journal of Phonetics, 40(1), 177-189.
7. Major, R. C. (1987). English voiceless stop production by speakers of Brazilian Portuguese. Journal of Phonetics, 15, 197—
8. Rekart, D. M. (1985) Evaluation of foreign accent using synthetic speech. Ph.D. dissertation, the Lousiana State University.
9. Boersma, P., & Weenink, D. (2014). Praat: Doing phonetics by computer (Version 5.4.04) [Computer program]. Retrieved
10. Hay, J., Podlubny, R., Drager, K., & McAuliffe, M. (under review). Car-talk: Location-specific speech production and
11. Lane, H., & Tranel, B. (1971). The Lombard sign and the role of hearing in speech. Journal of Speech, Language, and Hearing Research, 14(4), 677-709.
What the f***? Making sense of expletives in The Wire
Erica Gold – firstname.lastname@example.org
Dan McIntyre – email@example.com
University of Huddersfield
Huddersfield, HD1 3DH
Popular version of paper 3pSC87, “ What the f***? Making sense of expletives in ‘The Wire'”
Presented Wednesday afternoon, November 30, 2016
172nd ASA Meeting, Honolulu
In Season one of HBO’s acclaimed crime drama The Wire, Detectives Jimmy McNulty and ‘Bunk’ Moreland are investigating old homicide cases, including the murder of a young woman shot dead in her apartment. McNulty and Bunk visit the scene of the crime to try and figure out exactly how the woman was killed. What makes the scene unusual dramatically is that, engrossed in their investigation, the two detectives communicate with each other using only the word, “fuck” and its variants (e.g. motherfucker, fuckity fuck, etc.). Somehow, using only this vocabulary, McNulty and Bunk are able to communicate in a meaningful way. The scene is absorbing, engaging and even funny, and it leads to a fascinating question for linguists: how is the viewer able to understand what McNulty and Bunk mean when they communicate using such a restricted set of words?
To investigate this, we first looked at what other linguists have discovered about the word fuck. What is clear is that it’s a hugely versatile word that can be used to express a range of attitudes and emotions. On the basis of this research, we came up with a classification scheme which we then used to categorise all the variants of fuck in the scene. Some seemed to convey disbelief and some were used as insults. Some indicated surprise or realization while others functioned to intensify the following word. And some were idiomatic set phrases (e.g. Fuckin’ A!). Our next step was to see whether there was anything in the acoustic properties of the characters’ speech that would allow us to explain why we interpreted the fucks in the way that we did.
The entire conversation between Bunk and McNulty lasts around three minutes and contains a total of 37 fuck productions (i.e. variations of fuck). Due to the variation in the fucks produced, the one clear and consistent segment for each word was the <u> in fuck. Consequently, this became the focus of our study. The <u> in fuck is the same sound you find in the word strut or duck and is represented as /ᴧ/ in the International Phonetic Alphabet. When analysing vowel sounds, such as <u>, we can look at a number of aspects of its production.
In this study, we looked at the quality of the vowel by measuring the first three formants. In phonetics, the term formant refers to acoustic resonances of sound in the vocal tract. The first two formants can tell us if the production sounds more like, “fuck” rather than, “feck” or “fack,” and the third formant gives us information about the voice quality. We also looked at the duration of the <u> being produced, “fuuuuuck” versus “ fuck.”
After measuring each instance, we ran statistical tests to see if there was any relationship between the way in which it was said, and how we categorised its range of meanings. Our results showed that if we accounted for the differences in the vocal tract shapes of the actors playing Bunk and McNulty, the quality of the vowels are relatively consistent. That is, we get a lot of <u> sounds, rather than “eh,”“oo” or “ih.”
The productions of fucks that were associated with the category of realization were found to be very similar to those associated with disbelief. However, disbelief and realization did contrast with those that were used as insults, idiomatic phrases, or functional words. Therefore, it may be more appropriate to classify the meaning into fewer categories – those that signify disbelief or realization, and those that are idiomatic, insults, or functional. It is important to remember, however, that the latter group of three meanings are represented by fewer examples in the scene. Our initial results show that these two broad groups may be distinguished through the length of the vowel – short <u> is more associated with an insult, function, or idiomatic use rather than disbelief or surprise (for which the vowel tends to be longer). In the future, we would also like to analyse the intonation of the productions. See if you can hear the difference between these samples:
Example 1: realization/surprise
Example 2: general expletive which falls under the functional/idiomatic/insult category
Our results shed new light on what for linguists is an old problem: how do we make sense of what people say when speakers so very rarely say exactly what they mean? Experts in pragmatics (the study of how meaning is affected by context) have suggested that we infer meaning when people break conversational norms. In the example from The Wire, it’s clear that the characters are breaking normal communicative conventions. But pragmatic methods of analysis don’t get us very far in explaining how we are able to infer such a range of meaning from such limited vocabulary. Our results confirm that the answer to this question is that meaning is not just conveyed at the lexical and pragmatic level, but at the phonetic level too. It’s not just what we say that’s important, it’s how we fucking say it!
Noise, vibration, and harshness (NVH) of smartphones
Inman Jang – firstname.lastname@example.org
Tae-Young Park – email@example.com
Won-Suk Ohm – firstname.lastname@example.org
50, Yonsei-ro, Seodaemun-gu
Heungkil Park – email@example.com
Samsung Electro Mechanics Co., Ltd.
150, Maeyeong-ro, Yeongtong-gu
Suwon-si, Gyeonggi-do 16674
Popular version of paper 1aNS5, “Controlling smartphone vibration and noise”
Presented Monday morning, November 28, 2016
172nd ASA Meeting, Honolulu
Noise, vibration, and harshness, also known as NVH, refers to the comprehensive engineering of noise and vibration of a device through stages of their production, transmission, and human perception. NVH is a primary concern in car and home appliance industries because many consumers take into account the quality of noise when making buying decisions. For example, a car that sounds too quiet (unsafe) or too loud (uncomfortable) is a definite turnoff. That said, a smartphone may strike you as an acoustically innocuous device (unless you are not a big fan of Metallica ringtones), for which the application of NVH seems unwarranted. After all, who would expect the roar of a Harley from a smartphone? But think again. Albeit small in amplitude (less than 30 dB), smartphones emit an audible buzz that, because of the close proximity to the ear, can degrade the call quality and cause annoyance.
Figure 1: Smartphone noise caused by MLCCs
The major culprit for the smartphone noise is the collective vibration of tiny electronics components, known as multi-layered ceramic capacitors (MLCCs). An MLCC is basically a condenser made of piezoelectric ceramics, which expands and contracts upon the application of voltage (hence piezoelectric). A typical smartphone has a few hundred MLCCs soldered to the circuit board inside. The almost simultaneous pulsations of these MLCCs are transmitted to and amplified by the circuit board, the vibration of which eventually produces the distinct buzzing noise as shown in Fig. 1. (Imagine a couple hundred rambunctious little kids jumping up and down on a floor almost in unison!) The problem has been even more exacerbated by the recent trend in which the name of the game is “The slimmer the better”; because a slimmer circuit board is much easier to flex it transmits and produces more vibration and noise.
Recently, Yonsei University and Samsung Electromechanics in South Korea joined forces to address this problem. Their comprehensive NVH regime includes the visualization of smartphone noise and vibration (transmission), the identification and replacement of the most problematic MLCCs (production), and the evaluation of harshness of the smartphone noise (human perception). For visualization of smartphone noise, a technique known as the nearfield acoustic holography is used to produce a sound map as shown in Fig. 2, in which the spatial distribution of sound pressure, acoustic intensity or surface velocity can be overlapped on the snapshot of the smartphone. Such sound maps help smartphone designers draw a detailed mental picture of what is going on acoustically and proceed to rectify the problem by identifying the groups of MLCCs most responsible for producing the vibration of the circuit board. Then, engineers can take corrective actions by replacing the (cheap) problematic MLCCs with (expensive) low-vibration MLCCs. Lastly, the outcome of the noise/vibration engineering is measured not only in terms of physical attributes such as sound pressure level, but also in their psychological correlates such as loudness and the overall psychoacoustic annoyance. This three-pronged strategy (addressing production, transmission, and human perception) is proven to be highly effective, and currently Samsung Electromechanics is offering the NVH service to a number of major smartphone vendors around the world.
Popular version of poster, “Writer recognition with a sound in hand-writing”
172nd ASA Meeting, Honolulu
We can notice a car approaching by noise it makes on the road or can recognize a person by the sound of their footsteps. There are many studies analyzing and recognizing these noises. In the computer security industry, studies have even been proposed to estimate what is being typed from the sound of typing on the keyboard  and extracting RSA keys through noises made by a PC .
Of course, there is a relationship between a noise and its cause and that noise, therefore, contains information. The sound of a person writing, or “hand writing sound,” is one of the noises in our everyday environment. Previous studies have addressed the recognition of handwritten numeric characters by using the resulting sound, finding an average recognition of 88.4%. Based on this study, we seek the possibility of recognizing and identifying a writer by using the sound of their handwriting. If accurate identification is possible, it could become a method of signature verification without having to ever look at the signature.
We used the handwriting sounds of nine participants, conducting recognition experiments. We asked them to write the same text, which were names in Kanji, the Chinese characters, under several different conditions, such as writing slowly or writing on a different day. Figure 1 shows an example of a spectrogram of the hand-writing sound we analyzed. The bottom axis represents time and the vertical axis shows frequency. Colors represent the magnitude – or intensity – of the frequencies, where red indicates high intensity and blue is low.
The spectrogram showed features corresponding to the number of strokes in the Kanji. We used a recognition system based on a hidden Markov model (HMM) – typically used for speech recognition –, which represents transitions of spectral patterns as they evolve in time. The results showed an average identification rate of 66.3%, indicating that writer identification is possible in this manner. However, the identification rate decreased under certain conditions, especially a slow writing speed.
To improve performances, we need to increase the number of hand writing samples and include various written texts as well as participants. We also intend to include writing of English characters and numbers. We expect that Deep Learning, which is attracting increasing attention around the world, will also help us achieve a higher recognition rate in future experiments.
Zhuang, L., Zhou, F., and Tygar, J. D., Keyboard Acoustic Emanations Revisited, ACM Transactions on Information and Systems Security, 2009, vol.13, no.1, article 3, pp.1-26.
Genkin, D., Shamir, A., and Tromer, E., RSA Key Extraction via Low-Bandwidth Acoustic Cryptanalysis, Proceedings of CRYPTO 2014, 2014, pp.444-461.
Kitano, S., Nishino, T. and Naruse, H., Handwritten digit recognition from writing sound using HMM, 2013, Technical Report of the Institute of Electronics, Information and Communication Engineers, vol.113, no.346, pp.121-125.
Popular version of paper, 5aSC43, “Appropriateness of acoustic characteristics on perception of disaster warnings.”
Presented Friday morning, December 2, 2016
172nd ASA Meeting, Honolulu
As you might know, Japan has often been hit by natural disasters, such as typhoons, earthquakes, flooding, landslides, and volcanic eruptions. According to the Japan Institute of Country-ology and Engineering , 20.5% of all the M6 and greater earthquakes in the world occurred in Japan, and 0.3% of deaths caused by natural disasters worldwide were in Japan. These numbers seem quite high compared with the fact that Japan occupies only 0.28% of the world’s land mass.
Municipalities in Japan issue and announce evacuation calls to local residents through the community wireless system or home receiver when a disaster is approaching; however, there have been many cases reported in which people did not evacuate even after they heard the warnings . This is because people tend to not believe and disregard warnings due to a normalcy bias . Facing this reality, it is necessary to find a way to make evacuation calls more effective and trustworthy. This study focused on the influence of acoustic characteristics (voice gender, pitch, and speaking rate) of a warning call on the listeners’ perception of the call and tried to make suggestions for better communication.
Three short warnings were created: 1) Kyoo wa ame ga furimasu. Kasa wo motte dekakete kudasai. ‘It’s going to rain today. Please take an umbrella with you.’ 2) Ookina tsunami ga kimasu. Tadachini hinan shitekudasai. ‘A big tsunami is coming. Please evacuate immediately.’ and 3) Gakekuzure no kiken ga arimasu. Tadachini hinan shitekudasai. ‘There is a risk of landslide. Please evacuate immediately.’ A female and a male native speaker of Japanese, who both have relatively clear voices and good articulation, read the warnings out aloud at a normal speed (see Table 1 for the acoustic information of the utterances), and their utterances were recorded in a sound attenuated booth with a high quality microphone and recording device. Each of the female and male utterances was modified using the acoustic analysis software PRAAT  to create stimuli with 20% higher or lower pitch and 20% faster or slower speech rate. The total number of tokens created was 54 (3 warning types x 2 genders x 3 pitch levels x 3 speech rates), but only 4 of the warning 1) tokens were used in the perception experiment as practice stimuli.
Table 1: Acoustic Data of Normal Tokens
34 university students listened to each stimulus through the two speakers placed on the right and left front corners in a classroom (930cm x 1,500cm). Another group of 42 students and 11 people from the public listened to the same stimuli through one speaker placed on the front in a lab (510cm x 750cm). All of the participants rated each token on 1-to-5 scale (1: lowest, 5: highest) in terms of Intelligibility, Reliability, and Urgency.
Figure 1 summarizes the evaluation responses (n=87) in a bar chart, with the average scores calculated from the ratings on a 1-5 scale for each combination of the acoustic conditions. Taking Intelligibility, for example, the average score was the highest when the calls were spoken with a female voice, with normal speed and normal pitch. Similar results are seen for Reliability as well. On the other hand, respondents felt a higher degree of Urgency for both faster speed and higher pitch.
Figure 1. Evaluation responses (bar graph, in percent) and Average scores (data labels and
line graph on 1 – 5 scale)
The data were then analyzed with an analysis of variance (ANOVA, Table 2). Figure 2 illustrates the same results as bar charts. It was confirmed that for all of Intelligibility, Reliability, and Urgency, the main effect of speaking speed was the most dominant. In particular, Urgency can be influenced by the speed factor alone by up to 43%.
Table 2: ANOVA results
Figure 2: Decomposed variances in stacked bar charts based on the ANOVA results
Finally, we calculated the expected average evaluation scores, with respect to different levels of speed, to find out how much influence speed has on Urgency, with a female speaker and normal pitch (Figure 3). Indeed, by setting speed to fast, the perceived Urgency can be raised to the highest level, even at the expense of Intelligibility and Reliability to some degrees. Based on these results, we argue that the speech rate may effectively be varied depending on the purpose of an evacuation call, whether it prioritizes Urgency, or Intelligibility and Reliability.
Figure 3: Expected average evaluation scores on 1-5 scale, setting female voice and normal
Japan Institute of Country-ology and Engineering (2015). Kokudo wo shiru [To know the national land]. Retrieved from: http://www.jice.or.jp/knowledge/japan/commentary09.
2. Nakamura, Isao. (2008). Dai 6 sho Hinan to joho, dai 3 setsu Hinan to jyuumin no shinri [Chapter 6 Evacuation and Information, Section 3 Evacuation and Residents’ Mind]. In H. Yoshii & A. Tanaka (Eds.), Saigai kiki kanriron nyuumon [Introduction to Disaster Management Theory] (pp.170-176). Tokyo: Kobundo.
Drabek, Thomas E. (1986). Human System Responses to Disaster: An Inventory of Sociological Findings. NY: Springer-Verlag New York Inc.
Boersma, Paul & Weenink, David (2013). Praat: doing phonetics by computer [Computer program]. Retrieved from: http://www.fon.hum.uva.nl/praat/.
Popular version of paper 1pMU4, “Optimal insertion timing of symbolic music to induce laughter in video content.”
Presented Monday afternoon, November 28, 2016
172nd ASA Meeting, Honolulu
In television variety shows or comedy programs various sound effects and music are combined with humorous scenes to induce more pronounced laughter from viewers or listeners . The aim of our study was to clarify the optimum insertion timing of symbolic music to induce laughter in video contents. Symbolic music is music that is associated with a special meaning such as something funny as a sort of “punch line” to emphasize their humorous nature.
Fig. 1 Sequence of video and audio tracks in the video editing timeline
We conducted a series of rating experiments to explore the best timing for insertion of such music into humorous video contents. We also examined the affects of audiovisual contents. The experimental stimuli were four short video contents, which were created by mixing the two video (V1 & V2) and four music clips (M1, M2, M3 & M4).
The rating experiments clarified that insertion timing of symbolic music contributed to inducing laughter of video contents. In the case of a purely comical scene (V1), we found the optimal insertion time for high funniness rating was the shortest, at 0-0.5 seconds. In the case of a tragicomic scene, a humorous accident (V2), the optimal insertion time was longer, at 0.5-1 seconds after the scene; i.e., a short pause before the music was effective to increase funniness.
Fig. 2 Subjective evaluation value for the funniness in each insertion timing of symbolic music for each video scene.
Furthermore, the subjective evaluation value rating experiments showed that optimal timing was associated with the highest impressiveness of the videos, the highest evaluations, the highest congruence between moving pictures and sounds, and inducement of maximum laughter. We discovered all of the correlation coefficients are
very high, seen in the table summarizing the test.
Table 1 Correlation coefficient between the optimal timing for symbolic music and the affects for audiovisual contents.
** p< .01
In television variety shows or comedy programs, when symbolic music is dubbed over the video as a punch line just after the humorous scenes, insertion of a short pause of between half a second and a full second is very effective at emphasizing the humor of scenes, and increasing the impressiveness of viewer-listeners.
1. Kim, K.H., et al., F. Effectiveness of Sound Effects and Music to Induce Laugh in Comical Entertainment Television Show. The 13th International Conference on Music Perception and Cognition, 2014. CD-ROM.
2. Kim, K.H., et al., Effects of Music and Sound Effects to Increase Laughter in Television Programs. Media & Information Resources, 2014. 21(2): 15-28. (in Japanese with English abstract).