caa a22 4500 606160477 CHVBK 20210128100630.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00521-014-1708-8 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1708-8 Affect-insensitive speaker recognition systems via emotional speech clustering using prosodic features [Elektronische Daten] [Dongdong Li, Yubo Yuan, Zhaohui Wu, Yingchun Yang] Voice-based biometric security systems involving only neutral speech have achieved promising performance. However, the speakers are very likely to fail the recognition when the test data exhibit multiple emotions. This paper aimed to address the mismatch of the emotional states between training and testing speech. We discuss different modeling strategies that incorporate the emotions (affects) of speakers into the training stage of a Mandarin-based speaker recognition system and propose an alternative approach, which could optimize the utilization of the limited affective speech. The training speeches are partitioned and clustered by the trends of the prosodic variations. Multiple models are built based on the clustered speech for a given speaker. The prosodic differences are characterized by a combination of features that describe the changes of the fundamental frequencies and energy contours. The experiments were carried out based on the Mandarin Affective Speech Corpus. The result shows 73.37% improvement in recognition rate over that of the traditional speaker verification tasks relatively and also achieves 63.53% higher in performance over the structural training-based systems relatively. The Natural Computing Applications Forum, 2014 Speaker recognition nationallicence Emotional speech clustering nationallicence Prosodic features nationallicence Li Dongdong Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, China aut Yuan Yubo Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, China aut Wu Zhaohui Department of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China aut Yang Yingchun Department of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China aut Neural Computing and Applications Springer London 26/2(2015-02-01), 473-484 0941-0643 26:2<473 2015 26 521 https://doi.org/10.1007/s00521-014-1708-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1708-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li Dongdong Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, China aut NATIONALLICENCE 700 1- Yuan Yubo Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, China aut NATIONALLICENCE 700 1- Wu Zhaohui Department of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China aut NATIONALLICENCE 700 1- Yang Yingchun Department of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/2(2015-02-01), 473-484 0941-0643 26:2<473 2015 26 521