A new approach with score-level fusion for the classification of a speaker age and gender

Yucesoy E., NABIYEV V.

COMPUTERS & ELECTRICAL ENGINEERING, cilt.53, ss.29-39, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 53
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.compeleceng.2016.06.002
  • Dergi Adı: COMPUTERS & ELECTRICAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.29-39
  • Anahtar Kelimeler: Age and gender recognition, Spectral features, Prosodic features, Score-level fusion, Gaussian Mixture Model, Support Vector Machines, RECOGNITION, EXTRACTION
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

In this study a new approach for classifying speakers according to their age and genders is proposed. This approach is composed of score-level fusion of seven sub-systems. In this fused system, which provides improved performance in three classification categories (age, gender and age & gender), spectral and prosodic features extracted from short-duration phone conversations are used with Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and GMM supervector-based SVM classifiers. Also, by examining individual and various combinations of each system, the effect of feature types and classification methods on performance is investigated. With the proposed system, classification success rates are obtained 90.4%, 54.1%, and 53.5% in gender, age and age & gender categories respectively. (C) 2016 Elsevier Ltd. All rights reserved.