More on Triqui reduplication

Back in December, I posted a quick summary of this interesting pattern on Triqui verbs to Facebook. The post read as follows:

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So, I discovered a new pattern today. There is a process of partial reduplication on Triqui verbs that indicates something like an emphatic, but non-specific third person. The reduplicant is partially fixed, always containing tone /4/ and a coda /ʔ/, but the vowel identity (including nasality) is taken from the stem. The stem has to be marked with the non-specific 3rd person (which is marked by tone/glottal deletion).

/βĩ³/ 'to be/exist' > bĩ³-ĩʔ⁴
/a³βi³²/ 'to leave' > a³βi³-iʔ⁴
/tʃeh⁴/ 'to walk' > tʃe³-eʔ⁴
/a³tah³/ 'to say' > a³ta³-aʔ⁴   

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Well, as it turns out, the pattern doesn't quite just signify an emphatic. It appears to be a way of marking a wish/blessing on someone, e.g. "May they do X." Thus, to state "May they eat" or "Que comerían", the form is /tʃa²=aʔ⁴/ and "May they run" would be /ku²nã²=ãʔ⁴/. The interesting thing about these new forms is that both of the verbs occur in the potential aspect. The unmarked, progressive/habitual form for 'run' is /u⁴nãh³/ and the perfective form for 'eat' is /tʃa⁴³/. So, it is not clear if this reading of these forms just derives from the fact that they are in the potential aspect. This reading also appears to differ from the hortative, e.g. /tʃa²=yũʔ¹/ 'Let's eat!' (Incidentally, this is different from the regular /tʃa²=ũh⁴/ 'We will eat ~ We are eating.')

So, the mysteries now are: (a) Is there some semantic consistency when reduplication is used on verbs with varying aspects? (b) Might we be able to tease apart just what this means with unmarked aspect?

On a related note, one of the things I've come to realize about Triqui clitic morphology in doing documentation is that there is a very productive system for marking vocatives (or perhaps just 'terms of address') in the language. I used to think that only certain words had distinct, lexicalized vocative forms, e.g. /nni³/ 'mother' vs. /nnãh⁴³/ 'mother!', but it turns out that most names, and even clitics can undergo this process. This is done by a process that looks eerily like the reduplicative system above. You take the word, e.g. /tʃu³be³/ 'dog' and just change it's tone to /4/ and add a coda /ʔ/, e.g. /tʃu⁴beʔ⁴/ as in something like 'Hey, dog, how are you?'. One can do this with proper names too, e.g. 'Enrique' is /li⁴ki⁴³/ normally, but would be /li⁴kiʔ⁴/ when referred to directly. I have a feeling there is some relation between emphatic forms (maybe this is the good term for it after all?) and direct terms of address, but just how the expression of a wish/blessing fits in here is a mystery.

Incidentally, I learned the above form for 'dog' through a story about a magical dog who learned how to grind corn and make tortillas for a man who went to farm. When the man came into the house, he had some conversations with this dog and had to address it directly.

Ode to 2014

Here's to that email you never answered,
the message you never got,
still flagged and beeping,
haunting in being unknown
to all but the sender.
Nagging and distracting
like an unscratched lotto ticket
or a joke, unheard because you arrived too late.
"You had to be there"
but you were unavailable
as the message sits,
bored.

Whatever emergency we are waiting for
we wait too long,
with false flashing yanking us
to action-reaction,
seeing and skimming and sitting
and revealing to us no less than we expect;
minutiae.

The attack move to click
to see or hear what might be there
gives only a hasty rush
as we unfurl objects of glitter
to finally know no more than we did before.
Must a hamster run in its wheel
just because it's there?

If airlines were restaurants...

If airlines were restaurants...

We'd stand in line to get in,
but we could pay more to be put at the front of the line.
We'd be seated at a table of screaming children,
but we could pay more to get our own table.
We could also pay more to sit near a window
or in a "premium" seat near the fire escape.
But, just think, if we called in ahead of time to reserve a seat,
we wouldn't deal with such things.
...but we'd pay more to even do that.

We might wait for hours for our food
or suffer the injustice of being told
"no food today"
And we might have to go wait in another line.
Because it's not the cook's fault
that it's raining.

We could get a frequent diner card
and come in with a coupon
to be told that the terms had changed
because the money we once spent
at this fine dining establishment
was in the past.

If airlines were restaurants,
we might not visit them
or just decide to occasionally
visit the only other restaurant
in our town.

We might just make our own lunches
or forage for food in the streets
where the restaurants have ensured
a slow food movement.

Just don't complain to the chef or the wait staff.
You might not be allowed to come back.

Vocal fry doesn't harm your career prospects, but not being yourself just might

In a recent article in PLOS One, authors Anderson et al. find that, vocal fry is harmful to a woman's career prospects ("Vocal Fry May Undermine the Success of Young Women in the Labor Market"). At face value, the findings are surprising. When we perceive speech, we pay attention to very subtle cues and it's always a surprise when certain things that we hear are shown to have some hidden influence on our attitudes towards people. Moreover, anything to do with the influence of one's voice on employment prospects is similarly notable in a market where competition for jobs remains fierce.

Resultingly, this article has garnered some attention in the media recently (as in this Atlantic piece and this Marketplace piece) with the conclusion that women now need to police how they speak for fear of being perceived as untrustworthy by an employer. Yet, on closer inspection, it turns out that the police might not be needed at all. The original study contained quite serious flaws in its design which, when considered carefully, prevent us from making any conclusions about which specific acoustic characteristics sounded "untrustworthy" to the listeners who participated.

The design of the study was relatively straightforward. A group of 800 people, via an online system (Qualtrics), listened to speakers produce the sentence "Thank you for considering me for this opportunity." Some of these sentences were produced with vocal fry, which, in contrast to normal voice, involves temporal irregularity in the vibration of the vocal cords (folds) and lower overall pitch (see Figure 1 below). To a listener, the vocal folds sound like a stick being dragged along a fence, where one can hear individual vibrations or pulses of the vocal folds. The listeners were asked to evaluate speakers based on whether they were trustworthy, competent, educated, hireable, and attractive. The expectation in a study like this is that listeners might have different attitudes towards those sentences with vocal fry than they would towards sentences without vocal fry. The big issue here is just where the authors got the voices with vocal fry.

Figure 1: Example of regular (modal) vocal fold vibration and irregular vocal fold vibration (vocal fry) within the latter half of the word "opportunity".

When linguists, phoneticians, or speech scientists want to study whether an acoustic characteristic in someone's voice influences how listeners perceive them, they often will record a person and then modify those aspects of the person's voice which they wish to test. This process, called resynthesis, allows one to carefully control the acoustic dimensions in the signal and requires some knowledge of speech acoustics and digital signal processing. Certain aspects of one's voice are harder to modify than others. As it happens, vocal fry is one of these hard-to-modify characteristics. (I'll leave the more detailed question of why it is hard to resynthesize vocal fry and voice quality, more generally, out of the discussion for now.)

Fortunately, there is a solution. Just as one might buy two types of apples to compare their flavors, we can look for speakers who just happen to produce more vocal fry in their speech and compare them to those who do not produce it. If one were to play the speech of these two groups to listeners (and potential employers), listeners might have different attitudes about one of the groups. This is, in fact, what Yuasa (2010) did in her study of creaky phonation. Yet, importantly, the authors of the study here did no such thing. Rather, they recorded speakers producing normal utterances and then trained them to produce an utterance with greater vocal fry. As a consequence, the speech contained in all of the vocal fry stimuli is actually speech where speakers are attempting to imitate a voice with vocal fry. There are several reasons why this is problematic, but the first is perhaps the most obvious: most people are not particularly accurate at imitating someone else's speech. If you ask the average person to "talk like a Texan", they might (or might could) try to imitate something that they believe to be an important characteristic of Texas speech. Yet, to most listeners, especially those from Texas, they would sound like a caricature of an actual Texan.

As it turns out, this is the rub. While the speakers in the study here insert creak at various places in their speech, its real use in natural speech is more carefully controlled. Previous studies which look at vocal fry, particularly Redi and Shattuck-Hufnagel (2001), find that it is rather restricted. It tends to occur in locations in phrases and utterances where we might expect low pitch. Vocal fry is disconnected from these locations of low pitch in the imitated speech here. Rather, the speakers seem to produce a very flat, robotic voice when imitating vocal fry. The typical intonation for the stimulus sentence is something like "THANK you for conSIdering me FOR this OPorTUnity", where the syllables in caps reflect higher pitch levels than the surrounding ones.

This is not the only way in which the imitated speech sounds unnatural, however. With one exception (speaker 5), each of the imitated sentences produced by female speakers is also longer than the corresponding non-imitated sentence for that speaker, as shown in the table below:

	Sentence duration (vocal fry)	Sentence duration ("normal")
Speaker 1	2.91	2.25
Speaker 2	2.90	2.84
Speaker 3	2.69	2.19
Speaker 4	2.33	2.07
Speaker 5	2.15	2.37
Speaker 6	2.57	2.43
Speaker 7	3.24	2.57

These differences do not appear to be restricted to particular words either. As seen in Figure 2 (below), almost all words were longer in the imitated speech than in the natural speech. The longer duration here, in comparison with the shorter natural sentences, may have the quality of sounding stilted to the listener.

Figure 2: Duration of words in the sentence "Thank you for considering me for this opportunity" spoken by seven female speakers across two conditions: vocal fry (left) and normal voice (right). Note that the durations for all words are longer in the vocal fry condition.

A related problem in the study is the authors' acoustic analysis of the speech signal. The calculation of pitch in the speech signal requires determining how well successive vocal fold vibrations correlate with one another. When the vocal folds are vibrating normally, such a correlation is possible, but when vocal fold vibration is too irregular, as in vocal fry, it is impossible to calculate pitch accurately. However, an acoustic analysis program may still try to calculate possible (erroneous) values. Anderson et al. argue that the pitch in the vocal fry sentences is universally lower than that in the natural sentences, but they neither controlled nor mentioned how pitch was calculated during durations of vocal fry. In fact, the pitch on the expression "Thank you", which contained no vocal fry in any of the utterances, had universally lower pitch in the vocal fry sentences than in the normal sentences. This suggests that the speakers may simply be lowering pitch across the entire imitated sentence, rather than simply adding vocal fry. Finally, no quantitative acoustic estimation of actual vocal fry (such as jitter, shimmer, cepstral peak prominence, etc.) was ever included in the authors' study. Yes, you heard that right - in a study relating vocal fry to listener attitudes and hireability there was no actual estimation of whether the stimuli differed with respect to the test variable.

Taken together, these observations suggest that the speakers in the study simply attempted to lower their overall pitch level while imitating vocal fry rather than simply including more vocal fry. The increased effort involved in the imitation also made their utterances longer. These two acoustic differences, among others, would seem to contribute to the speakers sounding unnatural when imitating vocal fry. So, when listeners judge the female speakers with vocal fry as sounding "untrustworthy", there is a good possibility that they are simply making such a judgment based on the speaker not sounding like herself. The better lesson that one might take home instead here is that one's job prospects are harmed if you try to talk (or act) like someone who you are not.

References:
Anderson, R. C., Klofstad, C. A., Mayew, W. J., and Venkatachalam, M. (2014) Vocal fry may undermine the success of young women in the labor market. PLOS ONE 9(5): 1-8.

Redi, L. and Shattuck-Hufnagel, S. (2001) Variation in the realization of glottalization in normal speakers. Journal of Phonetics 29:407-429.

Yuasa, I. P. (2010) Creaky voice: a new feminine voice quality for young urban-oriented upwardly mobile American women? American Speech 85(3):315--337.

Visualizing vowel spaces in R: from points to contour maps

Typically when linguists wish to examine the vowels of a language, they plot the vowels in an F1xF2 space, which approximates a relative articulatory position of the vowels. Now, there are certainly problems with this approach (lack of F3, possible nasal formants, dynamic movement). Yet, despite these drawbacks, visualizing vowels this way is relatively standard and has the advantage of being understood by a wide audience. In R, there are several methods one might use to plot vowels in a space like this. I will discuss three here, two of which are clearly less than ideal and another which I am in the process of learning still. I will be relying on a data set of Arapaho vowels from elicitation sessions from three speakers. Given the nature of the data, I had to analyze a different number of vowels per speaker, so that one speaker is over-represented (1290 vowels) and two others are underrepresented (600-700 vowels each).

I am interested in visualizing the quality differences between long and short vowels in the language. Arapaho has short, long, and extra long vowels, though I only really have enough data to analyze long and short vowels, so I am sticking to that. I am looking at the monophthongs /i, ɛ, ɔ, u/, which are realized as more centralized variants when they are short. Here is a sample of my data:

Vquality Label Length Vaccent seg_Start  seg_End Duration Time       F1       F2       F3
8         o    v2  short       H  19985.83 20088.10  102.277    2 673.0124 1116.783 2887.543
9         o    v2  short       H  18887.63 18990.38  102.753    2 682.5495 1204.757 2636.614
10        o    v2  short       H  10048.30 10152.09  103.789    2 679.4601 1077.850 2910.295

I am leaving out several columns here (including speaker, word, etc.), but all data is coded for vowel, written i, e, o, u, and for length (short, long).

1. The first way to visualize a vowel space given data like this is to use R's plot function. The default here is to built up a plot by adding individual elements. With the data formatted the way it is, it would be necessary to create several subsets and plot each separately. For instance, if we restrict ourselves to just the long vowels, one could do the following:

> long <- subset(formant_data, Length=="long")
> u.long <- subset(long, Label=="u")
> i.long <- subset(long, Label=="i")
...

The result of this will be 4 different data frames, each of which could be plotted separately as points, as follows:

> par(mar=c(5, 4, 2, 2))

> plot(F1~F2, data=i.long, ylim=c(1000, 200), xlim=c(3000, 600), pch=1, col="red")

> points(F1~F2, data=e.long, pch=2, col="blue", add=T)

> points(F1~F2, data=o.long, pch=3, col="green", add=T)

> points(F1~F2, data=u.long, pch=4, col="black", add=T)

> legend("top", horiz=TRUE, c("i", "\u025B", "\u0254", "u"), pch=c(1, 2, 3, 4), col=c("red", "blue", "green", "black"), x.intersp=0.8)

This produces the following:

Fig 1

This looks good enough to plot observations, but what if one wants to get an idea about where the averages lie? It might be easy to imagine an average "i" here, but the other vowels seem somewhat dispersed throughout the vowel space (the short vowels are even more so). So, this is harder. 

2. One solution is to plot the vowel data using the vowelplot() function in the vowels package. This package can both draw circles around the vowel area and compute mean values for each of the vowels. However, it requires the user to reformat his/her data to fit a template used by the package. Depending on one's data organization, this can be cumbersome. The format of their template is a data frame of 9 columns, which includes: speaker_id, vowel_id, context, F1, F2, F3, F1_glide, F2_glide, and F3_glide. Plotting requires fewer commands, but the options within each command are limited. If we try the following:

> vowelplot(long, color="vowels", ylim=c(1000, 200), xlim=c(2600, 600)); it produces:

Fig 2

This figure is interesting insofar as it color codes the vowels and divides up the space by speaker. It does all this with a single command too. However, there is no way within the package to avoid dividing the data into speakers (as you might notice, I did not specify this in the plot command) when showing individual data points. 

Another advantage of this package is the ability to display vowel ellipses around the plotted data. We can do this with the following command added on:

> vowelplot(long, color="vowels", ylim=c(1000, 200), xlim=c(3000, 500))

> add.spread.vowelplot(long, ellipsis=TRUE, labels="vowels")

Fig 3

Ack! Yes, this is indeed very ugly. Unfortunately, the vowelplot() package always assumes that you want individual speakers. One could simply eliminate speaker differences in the data frame and replot the data with single ellipses. Alternately, one could plot the ellipses without the observations. I won't go into how to do this here. Instead, I will show one of the interesting advantages of using the vowelplot package: the ability to extract and plot mean formant values. So far, I have not plotted the short vowels because the degree of overlap would have been particularly large. I will do so here though:

These commands calculate the mean values for the first two formants among the short and long vowels.

> vlong <- compute.means(mono.long)

> vshort <- compute.means(mono.short)

These commands plot the data:

> vowelplot(vlong, label="vowels", ylim=c(800, 300), xlim=c(2400, 800), title="Vowel space in Arapaho")

> add.spread.vowelplot(vshort, labels="vowels")

This produces the following:

Fig 4

This looks much cleaner, but averages always look cleaner. The vowels with the dots represent the long vowels, while the vowels without the dots represent the short vowels. You'll notice that the short vowels are more centralized than the long vowels, though the back low vowel doesn't really change in quality. So far, so good.

Yet, as phoneticians (or as linguists/speech scientists), we are often more interested in the distribution of the data than the average values. Yet, if we plot ellipses here, it looks just as chaotic as Figure 3. This is because an ellipse contains two mutually perpendicular axes about which the ellipse is symmetric. These axes are the two dimensions (F1 and F2) which position the vowel in the vowel space. However, actual observations are not elliptically symmetrical around the center. Thus, ellipses might tend to overestimate the actual degree of overlap in a vowel space.

3. One solution to using the vowelplot package is to use ggplot2(). This very modern plotting software allows us a larger set of data visualization techniques. One way that I might think about plotting the distribution of my vowel data is with a two-dimensional contour map. Contour maps include three dimensions, with density as a "higher" point. They rely on kernel density estimation (KDE), which is a non-parametric way to estimate the probability density function of a random variable. Given that a prior distribution is not assumed, they also have the advantage of non-symmetry. As far as I know, I have not seen these applied to vowel spaces before. We can plot our data as follows:

> f.plot <- ggplot(formant_data, aes(x = F2, y = F1, color=factor(Vquality))) + geom_density2d(aes(label= factor(Vquality))) + scale_y_reverse() +  scale_x_reverse() + ylim(900, 200) + xlim(2800, 500)+ theme_bw() + scale_color_hue(name="Vowel quality", breaks=c("i", "e", "o", "u"), labels=c("i", "\u025B", "\u0254", "u"))

> f.plot 

This produces the following figure:

What this figure reveals that is so often left out of vowel plots is a clearer sense of the concentration of observations. One can observe a somewhat bimodal distribution for /u/, one concentrated with an F2 around 1000 Hz and another with an F2 around 1500 Hz. These probably reflect differences among speakers, but they may also reflect a difference of context (there is substantial alveolar fronting). If we wish to plot both short and long vowels, we can do so by using a facet_wrap() function. 

> f.plot <- ggplot(form.mono2, aes(x = F2, y = F1, color=factor(Vquality))) + geom_density2d(aes(label= factor(Vquality))) + scale_y_reverse() +  scale_x_reverse() + ylim(900, 200) + xlim(2800, 500)+ theme_bw() + scale_color_hue(name="Vowel quality", breaks=c("i", "e", "o", "u"), labels=c("i", "\u025B", "\u0254", "u")) + facet_wrap(~Length)

> f.plot 

This produces the following:

Now, we observe not only the tightness of observations around the median for the long vowels, but the asymmetrical ways in which the vowel space changes as a function of length. There are clear realizations of short /i/ which match those of long /i/ in quality. However, there are also a larger number which encroach into the center of the vowel space (though significantly more along the F1 dimension).

The advantage of using ggplot2() to show this data is that one can represent most of the observations and simultaneously observe non-linearities in the shape of the distribution. Outliers are more naturally excluded since they do not contribute to the estimated density function. By contrast, ellipses simply expand to symmetrically cover the entire space of the observations (or at least a space determined by symmetries inherent to normal distributions).

I think I am a fan of this method for vowel visualization, but I am unsure if this is the right way to go about things. Thus, any commentary is welcome.

It was never just about marriage.

Within the debate on same-sex marriage in the United States we have countless times heard the refrain that traditionalists are not homophobic, but rather wish the preserve the sanctity of the religious institution of marriage. This argument, in fact, was the main crux of the defense of Proposition 8 when it was debated in California in 2010. Now, as state after state considers its laws in light of the recent rulings that have severely weakened DOMA, one anticipates this argument to continue to come up on the state level. This argument is legally important, as it allows one to separate the preservation of the sanctity of marriage from the animus associated with homophobia. As the Proposition 8 and Supreme Court trials demonstrated, animus can not be used as a justification for the support of a law.

There is a dirty conceit that conservatives would do well to admit though: such a separation is simply an argument of convenience. To show this, let's begin by assuming that one can separate homophobia from a religious justification for traditional marriage. If this were true, one might expect that in states where traditional marriage was outlawed, other laws providing rights to gay and lesbian citizens would find more favor among local governments and voters. Yet, what we observe is the opposite: in states where gay marriage is forbidden, there has been substantial opposition to many other rights as well.

Simplifying a bit, the types of rights that gay and lesbian people have sought usually keep to the following trajectory. First, the right to consensual relations was sought. Second, non-discrimination in education, housing, or the workplace was sought. Third, some type of legal recognition (domestic partnership or civil union) for one's relationship was sought. Finally, the right to marry was sought. In those same states where same-sex marriage is outlawed and where its opponents argue that their position isn't driven by animus, one finds resistance to non-discrimination ordinances or even to consensual relations among same-sex partners (as in much of the southern US).

If it were just about the institution of marriage, and nothing else, then what on earth is the motivation for resisting the passage of all the other possible rights for gay and lesbian people? I don't know the conservative response to this question, but I would like to hear some rational argument if it exists. The notion that one can separate upholding the religious institution of marriage from a motivation from animus may sound good in theory, but actual practice shows us otherwise. And as a true empiricist (and positivist), I trust my observations.

If I were to call a spade a spade, I would say the separation argument is simply an attempt to save face in social circles in light of broader social acceptance of gay people and their relationships. After all, one can "hate the wedding but not the wedd-er" and still be in the in-crowd. Taken as such though, this sounds more like a social coping mechanism for someone uncomfortable with gay people than a legal argument used to oppose same-sex marriage.

R scripting problem

Maybe it's just something silly I can't figure out, but I've been banging my head at my computer for the past couple hours. So, I thought I would put this up on the web to elicit help. And yes, I have looked at stackoverflow and other sites for answers, but I've come up short so far.

Here's the issue:

Assume you have a data frame where "Time" values range from 1:10 and you have 3 measures (F1, F2, F3) for each 10 time point, e.g. F1 at time 1, F1 at time 2, etc. The goal is to take this data frame and simply print the mean value for each of the measures at each time point. If so, it should be possible to simply create subsets at each time point and then extract mean values for each measure. So, I wrote a script that does just this. It doesn't work though:

ts.obj <- ts(array(data=NA, dim=c(10, 3)))     #Create a time series for the output.
{for (i in 1:10)                            #Run a loop through each of the time points.
obj.i <- subset(object, Time==i)
mnF1 <- mean(obj.i$F1)           #Get mean values for each measure at time point "i."
mnF2 <- mean(obj.i$F2)
mnF3 <- mean(obj.i$F3)
ts.obj[i,1] <- mnF1                    #Place these mean values into the time series.
ts.obj[i,2] <- mnF2
ts.obj[i,3] <- mnF3 }

Any suggestions?