000			02047nam a2200361 i 4500
001			CR9781108552332
003			UkCbUP
005			20240730160754.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			170919s2020||||enk o ||1 0|eng|d
020			_a9781108552332 (ebook)
020			_z9781108428125 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aTK7882.S65 _bM35 2020
082	0	0	_a006.4/54 _223
100	1		_aMak, M. W., _eauthor. _974614
245	1	0	_aMachine learning for speaker recognition / _cMan-Wai Mak, Jen-Tzung Chien.
264		1	_aCambridge : _bCambridge University Press, _c2020.
300			_a1 online resource (xviii, 309 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 29 Jun 2020).
520			_aThis book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.
650		0	_aAutomatic speech recognition. _95558
650		0	_aBiometric identification. _911407
650		0	_aMachine learning. _91831
700	1		_aChien, Jen-Tzung, _eauthor. _974615
776	0	8	_iPrint version: _z9781108428125
856	4	0	_uhttps://doi.org/10.1017/9781108552332
942			_cEBK
999			_c84183 _d84183