000			03450nam a22003855i 4500
999			_c103712 _d103712 _x1
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005			20230102113143.0
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007			cr nn 008mamaa
008			150415s2015 gw | s |||| 0|eng d
020			_a9783319177250
024	7		_a10.1007/978-3-319-17725-0 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aTK7882.S65 _bR365 2015 EB
100	1		_aRao, K. Sreenivasa. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/no2004024924 _1http://viaf.org/viaf/65926948/
245	1	0	_aLanguage Identification Using Excitation Source Features _cby K. Sreenivasa Rao, Dipanjan Nandi.
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XII, 119 páginas 19 ilustraciones, 3 ilustraciones a color.)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aSpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning, _x2191-737X
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction -- Language Identification--A Brief Review -- Implicit Excitation Source Features for Language Identification -- Parametric Excitation Source Features for Language Identification -- Complementary and Robust Nature of Excitation Source Features for Language Identification -- Conclusion.
520	3		_aThis book discusses the contribution of excitation source information in discriminating language. The authors focus on the excitation source component of speech for enhancement of language identification (LID) performance. Language specific features are extracted using two different modes: (i) Implicit processing of linear prediction (LP) residual and (ii) Explicit parameterization of linear prediction residual. The book discusses how in implicit processing approach, excitation source features are derived from LP residual, Hilbert envelope (magnitude) of LP residual and Phase of LP residual; and in explicit parameterization approach, LP residual signal is processed in spectral domain to extract the relevant language specific features. The authors further extract source features from these modes, which are combined for enhancing the performance of LID systems. The proposed excitation source features are also investigated for LID in background noisy environments. Each chapter of this book provides the motivation for exploring the specific feature for LID task, and subsequently discuss the methods to extract those features and finally suggest appropriate models to capture the language specific knowledge from the proposed features. Finally, the book discuss about various combinations of spectral and source features, and the desired models to enhance the performance of LID systems.
988			_aEBSPRINGER_2018
650		7	_aIdentificación de personas _2embne _9679662
700	1		_aNandi, Dipanjan. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iEdición impresa: _z9783319177243
776	0	8	_iEdición impresa: _z9783319177267
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-17725-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2019 _dz _eIG _zSI