In This paper presents an overview of a stateof-the-art text-independent speaker verification system. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. First, an introduction proposes a modular scheme of the training and test phases of a speaker verification system. Then, the most commonly speech parameterization use...

Relevance Feedback (RF) has become a powerful technique in content-based image retrieval. Most RF methods assume that positive images follow the single Gaussian distribution, which is not sufficient to model the actual distribution of images due to the gap between the semantic concept and low-level features. In this paper, Gaussian mixture model (GMM) is applied to represent the distribution of...

The noisy short utterance is polluted by noise and corpus is less, so the recognition rate significantly decreased. For improving recognition rate, we proposed the dual information quality discrimination algorithm to classify the speech frames: one is differences detection and discrimination algorithm (DDADA), another is the improved SNR discrimination algorithm (ISNRDA). Based on the above two...

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...

In this paper, we present a speech enhancement technique based on the ambient noise classification that incorporates the Gaussian mixture model (GMM). The principal parameters of the statistical modelbased speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method and the long-term smoothing parameter of the noise estimation, are set according to the class...

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 ...

Speech recognition is the process of converting speech signals into words. For acoustic modeling HMM-GMM is used for many years. For GMM, it requires assumptions near the data distribution for calculating probabilities. For removing this limitation, GMM is replaced by DNN in acoustic model. Deep neural networks are the feed forward neural networks having more than one or multiple layers of hidd...

Dagenais (1999) and Lucchetti (2002) have demonstrated that the naive GMM estimator of Grogger (1990) for the probit model with an endogenous regressor is not consistent. This paper completes their discussion by explaining the reason for the inconsistency and presenting a natural solution. Furthermore, the resulting GMM estimator is analyzed in a Monte-Carlo simulation and compared with alterna...

Speaker verification is the process of accepting or rejecting claimed identity in terms of its sound features. A speaker verification system can be used for numerous security systems, including bank account accessing, getting to security points, criminology and etc. When a speaker verification system wants to check the identity of individuals remotely, it confronts problems such as noise effect...

This paper addresses the problem of fitting finite Gaussian Mixture Model (GMM) with unknown number of components to the univariate and multivariate data. The typical method for fitting a GMM is Expectation Maximization (EM) in which many challenges are involved i.e. how to initialize the GMM, how to restrict the covariance matrix of a component from becoming singular and setting the number of ...