We follow the correct Jagannathan and Wang (2002) framework for comparing the estimates and specification tests of the classical Beta and Stochastic Discount Factor/Generalized Method of Moments (SDF/GMM) methods. We extend previous studies by considering not only single but also multifactor models, and by taking into account some of the prescriptions for improving empirical tests suggested by ...

The length of the test speech greatly influences the performance of GMM-UBM based text-independent speaker recognition system, for example when the length of valid speech is as short as 1~5 seconds, the performance decreases significantly because the GMM-UBM based speaker recognition method is a statistical one, of which sufficient data is the foundation. Considering that the use of text inform...

The HMM (Hidden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian Mixture Model) is a finite mixture probability distribution model. Although the two models have a close relationship, they are always discussed independently and separately. The EM (Expectation-Maximum) algorithm is a general me...

Discriminative training (DT) methods of acoustic models, such as SVM and MMI-training GMM, have been proved effective in spoken language recognition. In this paper we propose a DT method for GMM using the large margin (LM) estimation. Unlike traditional MMI or MCE methods, the LM estimation attempts to enhance the generalization ability of GMM to deal with new data that exhibits mismatch with t...

To overcome the defects of common used algorithms based on model for abnormal speech recognition, which existed insufficient training data and difficult to fit each type of abnormal characters, an abnormal speech detection method based on GMM-UBM was proposed in this paper. For compensating the defects of methods based on model which difficult to deal with the diversification speech. Firstly, m...

This paper proposes an effective feature compensation scheme to address a real-life situation where clean speech database is not available for Gaussian Mixture Model (GMM) training for a model-based feature compensation method. The proposed scheme employs a Support Vector Machine (SVM)based model selection method to effectively generate the GMM for our feature compensation method directly from ...

It is meaningful to detect outliers in traffic data for traffic management. However, this is a massive task for people from large-scale database to distinguish outliers. In this paper, we present two methods: Kernel Smoothing Näıve Bayes (NB) method and Gaussian Mixture Model (GMM) method to automatically detect any hardware errors as well as abnormal traffic events in traffic data collected at...

In this paper, we propose a discriminative training (DT) method to alleviate the muffled sound effect caused by over smoothing in the Gaussian mixture model (GMM)-based voice conversion (VC). For the conventional GMM-based VC, we often observed a large degree of ambiguities among acoustic classes (generative classes), determined by the source feature vectors for generating the converted feature...

Although typical model-based noise suppression including the vector Taylor series-based approach employs a single Gaussian distribution for the noise model, it is insufficient for nonstationary noises which have a complex structured distribution. As a solution to this problem, we have already proposed a method for estimating a Gaussian mixture model (GMM)-based noise model by using a minimum me...

In this paper, we propose a novel voice conversion method called speaker model alignment (SMA), which does not require parallel training speech. Firstly, the source and target speaker models, described by Gaussian mixture model (GMM), are trained, respectively. Then, the transformation function of spectral features is learned by aligning the components of source and target speaker models iterat...