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In this paper, we study the use of two kinds of kernel-based discriminative models, namely support vector machine (SVM) and deep neural network (DNN), for speaker verification. We treat the verification task as a binary classification problem, in which a pair of two utterances, each represented by an i-vector, is assumed to belong to either the(More)
Anchorperson segment detection enables efficient video content indexing for information retrieval. Anchorperson detection based on audio analysis has gained popularity due to lower computational complexity and satisfactory performance. This paper presents a robust framework using a hybrid I-vector and deep neural network (DNN) system to perform anchorperson(More)
OBJECTIVE  This study focuses on the first (S1) and second (S2) heart sound recognition based only on acoustic characteristics; the assumptions of the individual durations of S1 and S2 and time intervals of S1-S2 and S2-S1 are not involved in the recognition process. The main objective is to investigate whether reliable S1 and S2 recognition performance can(More)
Alternative features were derived from extracted temporal envelope bank (TBANK). These simplified temporal representations were investigated in alignment procedures to generate frame-level training labels for deep neural networks (DNNs). TBANK features improved temporal alignments both for supervised training and for context dependent tree building.
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