Report

Quantifying Vowel Space Using Recordings of the IPA Vowels Bob Shackleton Congressional Budget Office Quantitative Linguistics and Dialectology University of Groningen June 29, 2012 Introduction • Quantifying relative distances between different sounds is a key requirement of dialectometry – for impressionistic records and modern acoustic analysis • Proposal: analyze multiple experts’ recordings of cardinal vowels from the International Phonetic Alphabet to characterize vowel space (extending work by Heeringa, Leinonen, and others) • Recordings by Jones, Ladefoged, Esling, Wells, House, and others • Use Praat software to develop cochleagrams of each recorded vowel • Calculate Euclidean distances between cochleagrams and apply 3D multidimensional scaling (MDS) to the distance matrix • Apply (rotated) factor analysis (FA) directly to cochleagrams to develop 3D characterization of vowel space • Use a simple characterization of factors to explore characterization • Basic Result: Analysis yields intuitively sensible 3-dimensional quantification of vowel space and distances among vowels Cochleagrams • 28 cochleagrams per recording per speaker (values in tenths of Barks) • Cochleagrams are variable for each recording and across speakers – total ~5,500 Bark/10 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 Hertz 103 198 297 403 517 641 778 931 1104 1298 1520 1773 2062 2392 2772 3209 3711 4289 4955 Multidimensional Scaling • Average together all speakers’ cochleagrams for each of the 28 IPA vowels; calculate a matrix of Euclidean distances between all the averages; apply MDS to averages, specifying 3 dimensions • Rotate and normalize MDS results so that [a] is at [0.0,0.0,0.0], [ɑ] is at [3.0,0.0,0.0], and [i] is on the x,y plane (i.e., open unrounded front to back distance = 3.0 and open unrounded front is directly “above,” as in traditional vowel diagram) • MDS dimensions 1 and 2 mainly characterize backing and height, respectively, dimension 3 mainly rounding (shape is basically a rectangular box with the closed back unrounded corner “smooshed in”) • Blue through green diamonds are front to back unrounded vowels; red through yellow circles are front to back rounded vowels Multidimensional Scaling • Dimension 3 mainly characterizes rounding: all unrounded vowels except for [ɤ] and [ɯ] take positive or near-positive values; rounded values uniformly non-positive • Matrix of Euclidean distances among vowels nearly perfectly correlated with matrix of Euclidean distances between average cochleagrams; that is, the MDS is replicating the relative distances among the original average cardinal vowels • Limitation of MDS: results cannot be easily applied to new data Multidimensional Scaling Multidimensional Scaling Multidimensional Scaling Where is Rounding? Rounding is characterized mainly as a deviation from unrounded intensities in the second formant – roughly Barks 9 through 14 or 1100 Hz through 2600 Hz) Factor Analysis: Factors • Perform factor analysis with varimax rotation on all 5000+ cochleagrams, specifying 2 or 3 factors • For 2 factors, loadings on cochleagrram intensities closely replicate Leinonen’s (2010) barkfilter results for Swedish speakers; if a 3rd factor is specified, it appears between the first two • Factor 1 captures variation mainly in Barks 14 through 20 (Formant 3 values – ~2400 Hz +) • Factor 2 captures variation mainly in Barks 5 through 9 (Formant 1 values – ~500 to 1100 Hz) • Factor 3 captures variation in Barks 10 through 13 (Formant 2 values – ~1100 to 2400 Hz), right where rounding differences appear Factor Analysis: Loadings • Again, average together all speakers’ scores for each vowel; normalize and rotate so that [a] is at [0.0,0.0,0.0], [ɑ] is at [3.0,0.0,0.0], and [i] is on the x,y plane • Reasonably good replication of original distances, but not as good as MDS: correlations with original data & with MDS distances are both about 0.9 (still get the same “smooshed box” shape) • Oblique rotation of factors yields almost exactly the same results, suggesting that factors are indeed essentially orthogonal • Factors can be applied to any new data to place it in the same grid A VERY Simple Characterization of Factors • Simplify the factors: calculate a Factor 2 /Formant 1 value that is just the average of cochleagram values 41 to 90; a Factor 3 / Formant 2 value that is the average of values 91 to 130; and a Factor 1 /Formant 3 that is the average of values 131 to 200 • Average together all speakers’ scores for each vowel; e.g. CFU [i] = 29.2, 25.2, 43.0 A VERY Simple Characterization of Factors • Again, rotate and normalize the values so that [a] is at [0.0,0.0,0.0], [ɑ] is at [3.0,0.0,0.0], and [i] is on the x,y plane • Better replication of original Euclidean distances than the factor analysis: correlation with data and with MDS distances = 0.97, correlation with factors = 0.85 • Can still be applied to any new data • Variance among speakers: the average standard deviations are 1.0 for Factor 1 / Formant 3, 0.5 for Factor 2 / Formant 1, and 0.9 for Factor 3 / Formant 2 Three-Dimensional Dispersion Three-Dimensional Dispersion Three-Dimensional Dispersion Conclusions • Can apply MDS, factor analysis, or even simpler calculations to cochleagrams of recordings of cardinal vowels to characterize vowel space • Each analysis yields intuitively sensible 3-dimensional characterization of vowel space and distances among vowels • Approaches yield closely correlated results, and are essentially consistent with formant analysis Data MDS Factors Simple Data 1.000 0.998 0.898 0.958 MDS 0.998 1.000 0.902 0.960 Factors Simple 0.898 0.958 0.902 0.960 1.000 0.850 0.850 1.000 • Results suggest that: • the entire spectrum is important, not just the formants, and • relative average intensities across a few broad ranges of frequencies contain most of the important information needed to process vowels • Factor results can be applied to any recording