Kye-Hwan Lee

A Support Vector Machine-Based Gender Identification Using Speech Signal

We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

Analysis and Improvement of Speech/Music Classification for 3GPP2 SMV Based on GMM

In this letter, a novel approach is proposed to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the Gaussian mixture model (GMM). An in-depth analysis of the features and classification method adopted in the conventional SMV is performed. Feature vectors applied to the GMM are then selected from the relevant parameters of the SMV for efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme implemented in the SMV.

A Support Vector Machine-Based Voice Activity Detection Employing Effective Feature Vectors

In this letter, we propose effective feature vectors to improve the performance of voice activity detection (VAD) employing a support vector machine (SVM), which is known to incorporate an optimized nonlinear decision over two different classes. To extract the effective feature vectors, we present a novel scheme that combines the a posteriori SNR, a priori SNR, and predicted SNR, widely adopted in conventional statistical model-based VAD.

Diffusion coefficient and solubility of vinyl acetate molecules in poly(vinyl acetate) matrices

Abstract Diffusion coefficient and equilibrium solubility for poly(vinyl acetate)/vinyl acetate system (VAc) were determined by measuring the rate of uptake or loss of VAc. The amount of residual VAc, as well as temperature, affected the mutual diffusion coefficient. Free volume theory, proposed recently by Vrentas and Duda, represented the behaviour of diffusion coefficients reasonably well over a wide range of temperature and VAc concentration.