Linguistics 520: Lab Assignment #5
10/2/2013 -- Due 10/16/2013
Goals:
1. Compare your vowel formants with the mean values in Hillenbrand 1995.
2. Normalize vowels as Z scores of formant values
3. Explore the "boy" and "girl" data in Hillenbrand
Put your formant measurements in a file "myvowels" that looks like this:
vowel dur F0 F1 F2 F3 F1.20 F2.20 F3.20 F1.50 F2.50 F3.50 F1.80 F2.80 F3.80 iy 153 117 252 2295 2945 273 2216 2891 251 2286 2938 266 2096 2765 ei 199 117 392 1949 2443 392 2013 2531 390 1956 2475 361 1998 2447 ae 210 99 673 1632 2316 599 1688 2328 675 1630 2314 665 1565 2358 ah 199 105 706 1199 2281 735 1280 2165 717 1219 2272 725 1354 2337 aw 204 104 613 945 2189 602 962 2195 619 1001 2209 620 1272 2184 oa 194 109 451 1005 2097 454 1008 2061 439 984 2131 409 1115 2125 uw 170 119 280 940 2125 288 989 2090 278 945 2129 283 1094 2078 ih 140 115 405 1844 2402 380 1935 2468 410 1835 2407 419 1706 2380 eh 146 106 533 1741 1924 475 1822 1984 567 1680 2269 537 1656 2307 uh 129 113 580 1265 2115 558 1305 2109 589 1296 2173 549 1427 2236 oo 120 109 416 1200 2165 387 1952 2195 422 1235 2157 412 1450 2183 er 187 111 418 1320 1578 437 1293 1608 415 1327 1613 406 1442 1680
Download the file h2.R to the same folder as your myvowels and htable files. Modify h2.R so that the line
vowelfile <- paste("YourNameHere","vowels",sep="_")
identifies you (via your "PennName").
If you execute h2.R in R, it will write out your vowel-formant data as "YourName_vowels" -- send this file to me as an email attachment.
The script h2.R will also produce the following four plots:
First, the average formant values for 12 English vowels, as pronounced by men and women in the Hillenbrand experiment, and as measured by Hillenbrand et al.:
Then a comparison of your vowels to those in the Hillenbrand data set:
Note that you should modify h2.R to compare your values to the appropriate set, so that the line that starts text(-MF2,-MF1, ... might use text(-WF2,-WF1, ... instead (and the plot title is also changed to supply your name):
MM <- read.table("myvowels",header=TRUE) MyF1 <- MM[,"F1"]; MyF2 <- MM[,"F2"] newyrange <- range(c(-MF1,-MyF1)) plot(-MF2,-MF1,type="n", ylim=newyrange, xlab="-F2", ylab="-F1", main="My data as per Praat\nCompared to Hillenbrand male speakers") text(-MF2,-MF1,labels=vowelIDs,adj=c(0.5,0.5),col="blue") lines(-MF2[1:7],-MF1[1:7], col="blue",lty=2) lines(-MF2[8:12],-MF1[8:12],col="blue",lty=3)
The third graph is a scatter-plot of all F1 and F2 values for all vowels for all the Hillenbrand men and women speakers, plotted as negative F2 against negative F1 so as to imitate the orientation of the IPA vowel quadrilateral:
The fourth graph is the same thing, plotting in Z-space after converting each individual's formant values relative to his or her mean and sd of F1 and F2:
Calculate the Z-score values for your own vowel data, and create a new plot comparing your Z-scores with those in the last graph. (You may find it useful to use the "cex" argument to the points() function, in order to make your values plainly visible -- and feel free to adjust colors and so on, to the same end.)
Now, choose your own methods to explore the "boy" and "girl" data in the Hillenbrand table. One obvious thing to do is just to plot the values -- mean values by vowels, scatter plots of all vowels -- of girls against boys, or of both against women and men.
Another possibility would be to expand tables like these:
Ratio (Women/Men) of average formant values from Hillenbrand et al.
iy | ei | ae | ah | aw | oa | uw | ih | eh | uh | oo | er | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
F1 | 1.28 | 1.12 | 1.14 | 1.22 | 1.23 | 1.12 | 1.21 | 1.13 | 1.24 | 1.22 | 1.11 | 1.10 |
F2 | 1.19 | 1.21 | 1.21 | 1.14 | 1.14 | 1.14 | 1.11 | 1.16 | 1.14 | 1.20 | 1.09 | 1.15 |
Ratios across all vowels:
F0 | F1 | F2 | F3 | |
---|---|---|---|---|
W/M Ratio | 1.68 | 1.18 | 1.17 | 1.12 |
And optionally, you can do some inferential statistics (is the difference between girls and boys statistically significant? What is the effect size? How do the differences compare to those between women and men?), or some statistical modeling (How do gender, age, and vowel identity interact? What about individual factors within gender and age groups?)