title: Cost-sensitive learning for emotion robust speaker recognition.
abstract: In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved.
authors.affiliation: School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
authors.forename: Dongdong
authors.initials: D
authors.lastname: Li
authors.affiliation: Department of Computer Science and Technology, Zhejiang University, No. 38, Yuquan Road, Zhejiang 310027, China.
authors.forename: Yingchun
authors.initials: Y
authors.lastname: Yang
authors.affiliation: School of Management, Fudan University, No. 220, Handan Road, Shanghai 200433, China.
authors.forename: Weihui
authors.identifier: 0000-0002-9426-5303
authors.initials: W
authors.lastname: Dai
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000295:instrumentation*; D004644:Emotions / Q000502:physiology*; D006801:Humans; D013060:Speech / Q000502:physiology*; D014831:Voice / Q000502:physiology*