Using Hierarchical Time Series Clustering Algorithm and Wavelet Classifier for Biometric Voice Classification.
Voice biometrics has a long history in biosecurity applications such as verification and identification based on characteristics of the human voice. The other application called voice classification which has its important role in grouping unlabelled voice samples, however, has not been widely studi...
| Publicado en: | Journal of Biomedicine & Biotechnology Vol. 2012; pp. 1 - 13 |
|---|---|
| Autor principal: | |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2012
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104298072&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104298072 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2012 vid: 2012 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104298072 104298072 2011906995 NLM22619492 PMC3351073 104298072 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Using Hierarchical Time Series Clustering Algorithm and Wavelet Classifier for Biometric Voice Classification. aug: au: Fong, Simon affil: Department of Computer and Information Science, University of Macau, Taipa, Macau sug: subj: Artificial Intelligence Biometrics Methods Clustering Algorithms Neural Networks (Computer) Signal Processing, Computer Assisted Voice Classification Human Resource Databases Decision Trees Male Probability Voice Recognition Systems Data Analysis Software Male ab: Voice biometrics has a long history in biosecurity applications such as verification and identification based on characteristics of the human voice. The other application called voice classification which has its important role in grouping unlabelled voice samples, however, has not been widely studied in research. Lately voice classification is found useful in phone monitoring, classifying speakers' gender, ethnicity and emotion states, and so forth. In this paper, a collection of computational algorithms are proposed to support voice classification; the algorithms are a combination of hierarchical clustering, dynamic time wrap transform, discrete wavelet transform, and decision tree. The proposed algorithms are relatively more transparent and interpretable than the existing ones, though many techniques such as Artificial Neural Networks, Support Vector Machine, and Hidden Markov Model (which inherently function like a black box) have been applied for voice verification and voice identification. Two datasets, one that is generated synthetically and the other one empirically collected from past voice recognition experiment, are used to verify and demonstrate the effectiveness of our proposed voice classification algorithm. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|