1887
banner image
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.
oa
On smoothing articulatory trajectories obtained from Gaussian mixture model based acoustic-to-articulatory inversion
Rent:
Rent this article for
Access full text Article
/content/asa/journal/jasa/134/2/10.1121/1.4813590
1.
1. S. Ouni and Y. Laprie, “ Studying pharyngealization using an articulograph,” International Workshop on Pharyngeals and Pharyngealisation (2009).
2.
2. S. J. Perkell, M. Cohen, M. Svirsky, M. Matthies, I. Garabieta, and M. Jackson, “ Electromagnetic mid-sagittal articulometer systems for transducing speech articulatory movements,” J. Acoust. Soc. Am. 92, 30783096 (1992).
http://dx.doi.org/10.1121/1.404204
3.
3. T. Shawker, M. Stone, and B. Sonies, “ Tongue pellet tracking by ultrasound: Development of a reverberation pellet,” J. Phonetics 13, 134146 (1985).
4.
4. T. Toda, A. Black, and K. Tokuda, “ Acoustic-to-articulatory inversion mapping with Gaussian mixture model,” in Proceedings of the ICSLP, Jeju Island, Korea (2004), pp. 11291132.
5.
5. P. K. Ghosh and S. S. Narayanan, “ A generalized smoothness criterion for acoustic-to-articulatory inversion,” J. Acoust. Soc. Am. 128(4 ), 21622172 (2010).
http://dx.doi.org/10.1121/1.3455847
6.
6. A. Toutios and K. Margaritis, “ Acoustic-to-articulatory inversion of speech: A review,” in Proceedings of the International 12th TAINN (2003).
7.
7. F. Faubel, J. McDonough, and D. Klakow, “ Bounded conditional mean imputation with Gaussian mixture models: A reconstruction approach to partly occluded features,” IEEE Trans. Acoust., Speech, Signal Process. 1, 38693872 (2009).
8.
8. R. M. Gray, “ Toeplitz and circulant matrices: A review,” Found. Trends Commun. Inf. Theory 2(3 ), 155329 (2005) (available at http://ee.stanford.edu/ gray/toeplitz.pdf).
http://dx.doi.org/10.1561/0100000006
9.
9. A. A. Wrench and H. J. William, “ A multichannel articulatory database and its application for automatic speech recognition,” in 5th Seminar on Speech Production: Models and Data, Bavaria (2000), pp. 305308.
10.
10. D. R. Cox and D. V. Hinkley, Theoretical Statistics (Chapman and Hall, London, 1974), Appendix 3.
http://aip.metastore.ingenta.com/content/asa/journal/jasa/134/2/10.1121/1.4813590
Loading
/content/asa/journal/jasa/134/2/10.1121/1.4813590
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/asa/journal/jasa/134/2/10.1121/1.4813590
2013-07-17
2014-12-19

Abstract

It is well-known that the performance of acoustic-to-articulatory inversion improves by smoothing the articulatory trajectories estimated using Gaussian mixture model (GMM) mapping (denoted by GMM + Smoothing). GMM + Smoothing also provides similar performance with GMM mapping using dynamic features, which integrates smoothing directly in the mapping criterion. Due to the separation between smoothing and mapping, what objective criterion GMM + Smoothing optimizes remains unclear. In this work a new integrated smoothness criterion, the smoothed-GMM (SGMM), is proposed. GMM + Smoothing is shown, both analytically and experimentally, to be identical to the asymptotic solution of SGMM suggesting GMM + Smoothing to be a near optimal solution of SGMM.

Loading

Full text loading...

/deliver/fulltext/asa/journal/jasa/134/2/1.4813590.html;jsessionid=g0ig532vixr6.x-aip-live-06?itemId=/content/asa/journal/jasa/134/2/10.1121/1.4813590&mimeType=html&fmt=ahah&containerItemId=content/asa/journal/jasa
true
true
This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: On smoothing articulatory trajectories obtained from Gaussian mixture model based acoustic-to-articulatory inversion
http://aip.metastore.ingenta.com/content/asa/journal/jasa/134/2/10.1121/1.4813590
10.1121/1.4813590
SEARCH_EXPAND_ITEM