Source microphone recognition aided by a kernel-based projection method

Abstract

Microphone recognition aims at recognizing different microphones based on the recorded speeches. In the literature, Gaussian Supervector (GSV) has been used as the feature vector representing a speech recording, which is obtained by adapting a universal background model (UBM). However, it is not clear how the performance of the GSV will be affected by the number of mixture components in the UBM. Besides, the raw GSV obtained from a speech recording contains both the microphone response information and the speech information, meaning that the raw GSV can be quite noisy as the feature vector for microphone recognition. In this paper, we investigate how GSV will be affected by the UBM and other parameters during the calculation of the GSV. In addition, in order to improve the quality of the raw GSV, we propose a kernel-based projection method to be applied to the raw GSV. This projection method maps the raw GSV onto another dimensional space. It is expected that in the projected feature space, the microphone response information and the speech information can be separated into different dimensions, meaning that the projected GSV should be better as the feature vector for microphone recognition compared to the raw GSV. Two classifiers that have been used in the literature, namely linear support vector machine (SVM) and sparse representation-based classifier (SRC), are employed to compare the performance of the raw GSV and the projected GSV. The experimental results demonstrate that the projected GSV can outperform the raw GSV no matter using linear SVM or SRC as the classifier, which shows the effectiveness of the projection method.