No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
Automatic assessment of vowel space area
1. G. Fant, Speech Sounds and Features (MIT Press, Cambridge, 1973).
3. R. L. Diehl, B. Lindblom, K. A. Hoemeke, and R. P. Fahey, “On explaining certain male-female differences in the phonetic realization of vowel categories,” J. Phonetics 24(2), 187–208 (1996).
4. R. A. Fox, “Perceptual structure of monophthongs and diphthongs in English,” Lang. Speech 26(1), 21–60 (1983).
5. J. Hillenbrand, L. A. Getty, M. J. Clark, and K. Wheeler, “Acoustic characteristics of American English vowels,” J. Acoust. Soc. Am. 97, 3099–3111 (1995).
6. E. Jacewicz and R. A. Fox, “The effects of cross-generational and cross-dialectal variation on vowel identification and classification,” J. Acoust. Soc. Am. 131(2), 1413–1433 (2012).
9. C. G. Clopper, D. B. Pisoni, and K. de Jong, “Acoustic characteristics of the vowel systems of six regional varieties of American English,” J. Acoust. Soc. Am. 118(3) 1661–1676 (2005).
11. J. Lee and S. Shaiman, “Relationship between articulatory acoustic vowel space and articulatory kinematic vowel space,” J. Acoust. Soc. Am. 132(3), 2003 (2012).
13. H. K. Vorperian and R. D. Kent, “Vowel acoustic space development in children: A synthesis of acoustic and anatomic data,” J. Speech Lang. Hear. Res. 50(6), 1510–1545 (2007).
16. L. B. Leonard, S. E. Weismer, C. A. Miller, D. J. Francis, J. B. Tomblin, and R. V. Kail, “Speed of processing, working memory, and language impairment in children,” J. Speech Lang. Hear. Res. 50(2), 408 (2007).
17. H.-M. Liu, F.-M. Tsao, and P. K. Kuhl, “The effect of reduced vowel working space on speech intelligibility in mandarin-speaking young adults with cerebral palsy,” J. Acoust. Soc. Am. 117, 3879–3889 (2005).
18. S. Sapir, L. O. Ramig, J. L. Spielman, and C. Fox, “Formant centralization ratio: A proposal for a new acoustic measure of dysarthric speech,” J. Speech Lang. Hear. Res. 53(1), 114 (2010).
19. Y.-I. Bang, K. Min, Y. H. Sohn, and S.-R. Cho, “Acoustic characteristics of vowel sounds in patients with Parkinson disease,” J. Speech, Lang. Hear. Res. 47, 766–783 (2013).
21. P. A. McRae, K. Tjaden, and B. Schoonings, “Acoustic and perceptual consequences of articulatory rate change in Parkinson disease,” J. Speech Lang. Hear. Res. 45(1), 35–50 (2002).
22. G. S. Turner, K. Tjaden, and G. Weismer, “The influence of speaking rate on vowel space and speech intelligibility for individuals with amyotrophic lateral sclerosis,” J. Speech Hear. Res. 38(5), 1001–1013 (1995).
23. G. Weismer, J.-Y. Jeng, J. Laures, R. D. Kent, and J. F. Kent, “Acoustic and intelligibility characteristics of sentence production in neurogenic speech disorders,” Folia Phoniatr. Logop. 53, 1–18 (2001).
24. C. M. Higgins and M. M. Hodge, “Vowel area and intelligibility in children with and without dysarthria,” J. Med. Speech Lang. Pathol. 10(4), 271–278 (2002).
25. S. Sapir, J. L. Spielman, L. O. Ramig, B. H. Story, and C. Fox, “Effects of intensive voice treatment (the Lee Silverman voice treatment [LSVT]) on vowel articulation in dysarthric individuals with idiopathic Parkinson disease: Acoustic and perceptual findings,” J. Speech Lang. Hear. Res. 50(4), 899–912 (2007).
26. K. L. Lansford and J. M. Liss, “Vowel acoustics in dysarthria: Speech disorder diagnosis and classification,” J. Speech Lang. Hear. Sci., in press.
27. K. L. Lansford and J. M. Liss, “Vowel acoustics in dysarthria: Mapping to perception,” J. Speech Lang. Hear. Sci., in press.
28. J. Rusz, R. Cmejla, T. Tykalova, H. Ruzickova, J. Klempir, V. Majerova, J. Picmausova, J. Roth, and E. Ruzicka, “Imprecise vowel articulation as a potential early marker of Parkinson's disease: Effect of speaking task,” J. Acoust. Soc. Am. 134, 2171–2181 (2013).
29. W. M. Fisher, G. R. Doddington, and K. M. Goudie-Marshall, “The DARPA speech recognition research database: Specifications and status,” in Proceedings of DARPA Workshop on Speech Recognition (1986), pp. 93–99.
30. P. Boersma, “praat, a system for doing phonetics by computer,” Glot Int. 5(9/10), 341–345 (2001).
31. D. G. Childers, Modern Spectrum Analysis, IEEE Press Selected Reprint Series (IEEE, New York, 1978).
32. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in c, The Art of Scientific Computing, 2nd ed. (Cambridge University Press, New York, 1992).
33. D. A. Reynolds and R. C. Rose, “Robust text-independent speaker identification using Gaussian mixture speaker models,” IEEE Trans. Speech Audio Process. 3(1), 72–83 (1995).
34. J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, edited by L. M. Le Cam and J. Neyman (University of California Press, Berkley, 1967), Vol. 1, pp. 281–297.
35. F. P. Preparata and M. I. Shamos, “Introduction,” in Computational Geometry, Texts and Monographs in Computer Science (Springer, New York, 1985), pp. 1–35.
36. matlab, version 18.104.22.1683 (R2012b), The MathWorks Inc., Natick, Massachusetts, 2012.
37. Language Files, edited by C. J. Colby, R. Wallace, and C. Jolly (Ohio State University Press, Columbus, 1982).
38. K. Bunton and G. Weismer, “The relationship between perception and acoustics for a high-low vowel contrast produced by speakers with dysarthria,” J. Speech Lang. Hear. Res. 44(6), 1215–1228 (2001).
Article metrics loading...
Vowel space area (VSA) is an attractive metric for the study of speech production deficits and reductions in intelligibility, in addition to the traditional study of vowel distinctiveness. Traditional VSA estimates are not currently sufficiently sensitive to map to production deficits. The present report describes an automated algorithm using healthy, connected speech rather than single syllables and estimates the entire vowel working space rather than corner vowels. Analyses reveal a strong correlation between the traditional VSA and automated estimates. When the two methods diverge, the automated method seems to provide a more accurate area since it accounts for all vowels.
Full text loading...
Most read this month