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008			230503s2012 sz | o |||| 0|eng d
020			_a9783031025631
024	7		_a10.1007/978-3-031-02563-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7882.S65 _b2012 EB
100	1		_aLevinson, Stephen C. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _98455
245	1	0	_aArticulatory Speech Synthesis from the Fluid Dynamics of the Vocal Apparatus _cby Stephen Levinson, Don Davis, Scott Slimon, Jun Huang
250			_a1st edition 2012
264		1	_aCham _bSpringer International Publishing _c2012
300			_a1 recurso en línea (XII, 104 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Speech and Audio Processing _x1932-1678
505	0		_aIntroduction -- Literature Review -- Estimation of Dynamic Articulatory Parameters -- Construction of Articulatory Model Based on MRI Data -- Vocal Fold Excitation Models -- Experimental Results of Articulatory Synthesis -- Conclusion.
520			_aThis book addresses the problem of articulatory speech synthesis based on computed vocal tract geometries and the basic physics of sound production in it. Unlike conventional methods based on analysis/synthesis using the well-known source filter model, which assumes the independence of the excitation and filter, we treat the entire vocal apparatus as one mechanical system that produces sound by means of fluid dynamics. The vocal apparatus is represented as a three-dimensional time-varying mechanism and the sound propagation inside it is due to the non-planar propagation of acoustic waves through a viscous, compressible fluid described by the Navier-Stokes equations. We propose a combined minimum energy and minimum jerk criterion to compute the dynamics of the vocal tract during articulation. Theoretical error bounds and experimental results show that this method obtains a close match to the phonetic target positions while avoiding abrupt changes in the articulatory trajectory. The vocal folds are set into aerodynamic oscillation by the flow of air from the lungs. The modulated air stream then excites the moving vocal tract. This method shows strong evidence for source-filter interaction. Based on our results, we propose that the articulatory speech production model has the potential to synthesize speech and provide a compact parameterization of the speech signal that can be useful in a wide variety of speech signal processing problems. Table of Contents: Introduction / Literature Review / Estimation of Dynamic Articulatory Parameters / Construction of Articulatory Model Based on MRI Data / Vocal Fold Excitation Models / Experimental Results of Articulatory Synthesis / Conclusion.
988			_aSynthesis Collection of Technology_2012
650		7	_2embne _9147323 _aReconocimiento automático del lenguaje
700	1		_aDavis, Don _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688329
700	1		_aSlimon, Scot _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688330
700			_aHuang, Jun _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9100290
776	0	8	_iPrinted edition: _z9783031014352
776	0	8	_iPrinted edition: _z9783031036910
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02563-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eIG _zSI