Classifying Human Voices by Using Hybrid SFX Time-Series Preprocessing and Ensemble Feature Selection
Joint Authors
Wong, Raymond
Lan, Kun
Fong, Simon
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-27, 27 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-10-29
Country of Publication
Egypt
No. of Pages
27
Main Subjects
Abstract EN
Voice biometrics is one kind of physiological characteristics whose voice is different for each individual person.
Due to this uniqueness, voice classification has found useful applications in classifying speakers’ gender, mother tongue or ethnicity (accent), emotion states, identity verification, verbal command control, and so forth.
In this paper, we adopt a new preprocessing method named Statistical Feature Extraction (SFX) for extracting important features in training a classification model, based on piecewise transformation treating an audio waveform as a time-series.
Using SFX we can faithfully remodel statistical characteristics of the time-series; together with spectral analysis, a substantial amount of features are extracted in combination.
An ensemble is utilized in selecting only the influential features to be used in classification model induction.
We focus on the comparison of effects of various popular data mining algorithms on multiple datasets.
Our experiment consists of classification tests over four typical categories of human voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition.
The experiments yield encouraging results supporting the fact that heuristically choosing significant features from both time and frequency domains indeed produces better performance in voice classification than traditional signal processing techniques alone, like wavelets and LPC-to-CC.
American Psychological Association (APA)
Fong, Simon& Lan, Kun& Wong, Raymond. 2013. Classifying Human Voices by Using Hybrid SFX Time-Series Preprocessing and Ensemble Feature Selection. BioMed Research International،Vol. 2013, no. 2013, pp.1-27.
https://search.emarefa.net/detail/BIM-1030837
Modern Language Association (MLA)
Fong, Simon…[et al.]. Classifying Human Voices by Using Hybrid SFX Time-Series Preprocessing and Ensemble Feature Selection. BioMed Research International No. 2013 (2013), pp.1-27.
https://search.emarefa.net/detail/BIM-1030837
American Medical Association (AMA)
Fong, Simon& Lan, Kun& Wong, Raymond. Classifying Human Voices by Using Hybrid SFX Time-Series Preprocessing and Ensemble Feature Selection. BioMed Research International. 2013. Vol. 2013, no. 2013, pp.1-27.
https://search.emarefa.net/detail/BIM-1030837
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1030837