000			03677nam a22005175i 4500
999			_c105995 _d105995
001			978-1-4419-8447-0
003			DE-He213
005			20190911143758.0
007			cr nn 008mamaa
008			110806s2011 xxu| s |||| 0|eng d
020			_a0321714510
024	7		_a10.1007/978-1-4419-8447-0 _2doi
040			_cTUK
050		4	_aP126 _b.C66 2012
100	1		_aO' Grady William _937685
245	1	0	_aContemporary linguistic analysis _cWilliam O'Grady _ban introduction
260			_ausa _bpearson _c2012
300			_axix,574p _c26cm
490	1		_aSpringerBriefs in Electrical and Computer Engineering
505	0		_aBasic Concepts and Applicability of Speech Parameterization -- Survey on speech parameterization -- Fourier transform based methods -- Wavelet packets based methods -- Evaluation on the speech recognition task -- Evaluation on the speaker recognition task -- Practical considerations -- Links to code and further sources of information.
520			_aContemporary Methods for Speech Parameterization offers a general view of short-time cepstrum-based speech parameterization and provides a common ground for further in-depth studies on the subject. Specifically, it offers a comprehensive description, comparative analysis, and empirical performance evaluation of eleven contemporary speech parameterization methods, which compute short-time cepstrum-based speech features. Among these are five discrete wavelet packet transform (DWPT)-based, six discrete Fourier transform (DFT)-based speech features and some of their variants which have been used on the speech recognition, speaker recognition, and other related speech processing tasks. The main similarities and differences in their computation are discussed and empirical results from performance evaluation in common experimental conditions are presented. The recognition accuracy obtained on the monophone recognition, continuous speech recognition and speaker recognition tasks is contrasted against the one obtained for the well-known and widely used Mel Frequency Cepstral Coefficients (MFCC). It is shown that many of these methods lead to speech features that do offer competitive performance on a certain speech processing setup when compared to the venerable MFCC. The last does not target the promotion of certain speech features but instead aims to enhance the common understanding about the advantages and disadvantages of the various speech parameterization techniques available today and to provide the basis for selection of an appropriate speech parameterization in each particular case.
650		0	_aEngineering. _93136
650		0	_aComputer science. _9231
650		0	_aTranslators (Computer programs). _938090
650	1	4	_aEngineering. _93136
650	2	4	_aSignal, Image and Speech Processing. _938091
650	2	4	_aLanguage Translation and Linguistics. _938092
650	2	4	_aUser Interfaces and Human Computer Interaction. _938093
710	2		_aSpringerLink (Online service) _933414
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781441984463
830		0	_aSpringerBriefs in Electrical and Computer Engineering _938094
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4419-8447-0
942			_2lcc _cBK