During the last few years, the speaker recognition technique has been widely attractive for its extensive application in many fields, such as speech communications, domestics services, and smart terminals. As a critical method, the Gaussian mixture model (GMM) makes it possible to achieve the recognition capability that is close to the hearing ability of human in a long speech. However, the GMM is failing to recognize a short utterance speaker with a high accuracy. Aiming at solving this problem, in this paper, we propose a novel model to enhance the recognition accuracy of the short utterance speaker recognition system. Different from traditional models based on the GMM, we design a method to train a convolutional neural network to process spectrograms, which can describe speakers better. Thus, the recognition system gains the considerable accuracy as well as the reasonable convergence speed. The experiment results show that our model can help to decrease the equal error rate of the recognition from 4.9% to 2.5%.

GMM and CNN Hybrid Method for Short Utterance Speaker Recognition

Establishing a software defect prediction model via effective dimension reduction

Dimensionality reduction techniques play important roles in the analysis of big data. Traditional dimensionality reduction approaches, such as principal component analysis (PCA) and linear discriminant analysis (LDA), have been studied extensively in the past few decades. However, as the dimensionality of data increases, the computational cost of traditional dimensionality reduction methods grows exponentially, and the computation becomes prohibitively intractable. These drawbacks have triggered the development of random projection (RP) techniques, which map high-dimensional data onto a low-dimensional subspace with extremely reduced time cost. However, the RP transformation matrix is generated without considering the intrinsic structure of the original data and usually leads to relatively high distortion. Therefore, in recent years, methods based on RP have been proposed to address this problem. In this paper, we summarize the methods used in different situations to help practitioners to employ the proper techniques for their specific applications. Meanwhile, we enumerate the benefits and limitations of the various methods and provide further references for researchers to develop novel RP-based approaches.

A survey of dimensionality reduction techniques based on random projection.

Sparse Algorithm for Robust LSSVM in Primal Space

Smooth pinball loss nonparallel support vector machine for robust classification

Extended compressed tracking via random projection based on MSERs and online LS-SVM learning

The invention discloses a real-time tracking method based on a stable appearance model. The method comprises the steps: firstly extracting MSERs information of a tracked region; carrying out the projection of the information based on a sparse random matrix meeting the requirements of RIP according to the above; obtaining more stable adaptive appearance; and finally carrying out the real-time tracking of a target through an online Bayes classifier. According to the invention, the stable region of the target is extracted so as to reduce the random impact from the sparse random projection, thereby achieving the more stable description of the appearance information of the target.

Real-time tracking method based on stable appearance model

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Extended compressed tracking via random projection based on MSERs and online LS-SVM learning

Real-time tracking method based on stable appearance model