Using vector of fractal dimensions for feature reduction and phoneme recognition and classification

Author

Hosseini, S.A. ; Ghassemian, Hassan ; Alizadeh, Rana

Author_Institution

Fac. of Electr. & Comput. Eng., Tarbiat Modares Univ., Tehran, Iran

fYear

2012

fDate

20-22 Nov. 2012

Firstpage

748

Lastpage

751

Abstract

Difference between Hausdorff fractal dimensions of phonemes gave us a motivation to use this feature as input of a statistical Bayesian classification system and a nearest neighborhood (NN) classifier for speech waveform recognition. We divide phoneme waveforms to adjacent segments and calculate Hausdorff fractal dimension of each segment and using them as the input of a Bayesian/Nearest Neighborhood classifier. The power point of algorithm is in consideration of order of samples information in contrast of other non-supervised feature extraction algorithms.

Keywords

Bayes methods; feature extraction; speech recognition; Hausdorff fractal dimensions; feature reduction; nearest neighborhood classifier; nonsupervised feature extraction algorithms; phoneme classification; phoneme recognition; speech waveform recognition; statistical Bayesian classification system; Bayesian methods; Classification algorithms; Fractals; Principal component analysis; Speech; Speech recognition; Support vector machine classification; Classification; Feature extraction; Fractal dimension; Phoneme; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Telecommunications Forum (TELFOR), 2012 20th

Conference_Location

Belgrade

Print_ISBN

978-1-4673-2983-5

Type

conf

DOI

10.1109/TELFOR.2012.6419316

Filename

6419316