000			03517nam a22005295i 4500
001			978-3-319-17725-0
003			DE-He213
005			20200421111843.0
007			cr nn 008mamaa
008			150415s2015 gw | s |||| 0|eng d
020			_a9783319177250 _9978-3-319-17725-0
024	7		_a10.1007/978-3-319-17725-0 _2doi
050		4	_aTK5102.9
050		4	_aTA1637-1638
050		4	_aTK7882.S65
072		7	_aTTBM _2bicssc
072		7	_aUYS _2bicssc
072		7	_aTEC008000 _2bisacsh
072		7	_aCOM073000 _2bisacsh
082	0	4	_a621.382 _223
100	1		_aRao, K. Sreenivasa. _eauthor.
245	1	0	_aLanguage Identification Using Excitation Source Features _h[electronic resource] / _cby K. Sreenivasa Rao, Dipanjan Nandi.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015.
300			_aXII, 119 p. 19 illus., 3 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112
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			_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.
650		0	_aEngineering.
650		0	_aComputational linguistics.
650	1	4	_aEngineering.
650	2	4	_aSignal, Image and Speech Processing.
650	2	4	_aLanguage Translation and Linguistics.
650	2	4	_aComputational Linguistics.
700	1		_aNandi, Dipanjan. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319177243
830		0	_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-17725-0
912			_aZDB-2-ENG
942			_cEBK
999			_c55653 _d55653