Support vector learning for gender classification using audio and visual cues
Abstract
The paper investigated gender classification using Support Vector Machines (SVMs). The visual (thumbnail frontal face) and the audio (features from speech data) cues were considered for designing the classifier. Three different representations of the data, namely, raw data, principal component analysis (PCA) and non-negative matrix factorization (NMF) were used for the experimentation with visual signal. For speech, mel-cepstral coefficient and pitch were used for the experimentation. It was found that the best overall classification rates obtained using SVM for the visual and speech data were 95.31% and 100%, respectively, on data set collected in laboratory environment.
Publication Title
International Journal of Pattern Recognition and Artificial Intelligence
Recommended Citation
Walavalkar, L., Yeasin, M., Narasimhamurthy, A., & Sharma, R. (2003). Support vector learning for gender classification using audio and visual cues. International Journal of Pattern Recognition and Artificial Intelligence (3), 417-439. https://doi.org/10.1142/S0218001403002447
