11 THE GMM ESTIMATION 2 11.1 Consistency and Asymptotic Normality . . . . . . . . . . . . . . . . . . . . . 3 11.2 Regularity Conditions and Identification . . . . . . . . . . . . . . . . . . . . . 4 11.3 The GMM Interpretation of the OLS Estimation . . . . . . . . . . . . . . . . . 5 11.4 The GMM Interpretation of the MLE . . . . . . . . . . . . . . . . . . . . . . . 6 11.5 The GMM Estimation ...

It is well-known that the performance of acoustic-to-articulatory inversion improves by smoothing the articulatory trajectories estimated using Gaussian mixture model (GMM) mapping (denoted by GMM + Smoothing). GMM + Smoothing also provides similar performance with GMM mapping using dynamic features, which integrates smoothing directly in the mapping criterion. Due to the separation between smo...

Language recognition with support vector machines and shifteddelta cepstral features has been an excellent performer in NISTsponsored language evaluation for many years. A novel improvement of this method has been the introduction of hybrid SVM/GMM systems. These systems use GMM supervectors as an SVM expansion for classification. In prior work, methods for scoring SVM/GMM systems have been int...

Gaussian mixture models (GMMs) have proven extremely successful for textindependent speaker verification. The standard training method for GMM models is to use MAP adaptation of the means of the mixture components based on speech from a target speaker. In this work we look into the various models (GMM-UBM and GMM-SVM) and their application to speaker verification. In this paper, features vector...

The paper considers text independent speaker identification over the telephone using short training and testing data. Gaussian Mixture Modeling (GMM) is used in the testing phase, but the parameters of the model are taken from clusters obtained for the training data by an adequate choice of feature vectors and a distance measure without optimization in the maximum likelihood (ML) sense. This di...

This paper proposes a new mapping method combining GMM and codebook mapping methods to transform spectral envelope for voice conversion system. After analyzing overly smoothing problem of GMM mapping method in detail, we propose to convert the basic spectral envelope by GMM method and convert envelope-subtracted spectral details by GMM and phone-tied codebook mapping method. Objective evaluatio...

This paper presents a new approach to modeling speech spectra and pitch for text-independent speaker identification using Gaussian mixture models based on multi-space probability distribution (MSD-GMM). The MSD-GMM allows us to model continuous pitch values for voiced frames and discrete symbols representing unvoiced frames in a unified framework. Spectral and pitch features are jointly modeled...

Impressive progress has been recently witnessed on deep unsupervised clustering and feature disentanglement. In this paper, we propose a novel method top of one recent architecture with explanation Gaussian mixture model (GMM) membership, accompanied by GMM loss to enhance the clustering. The is optimized explicitly computed parameters under our coupled inspired framework. Specifically, takes a...

The Gaussian mixture modeling (GMM) techniques are increasingly being used for both speaker identification and verification. Most of these models assume diagonal covariance matrices. Although empirically any distribution can be approximated with a diagonal GMM, a large number of mixture components are usually needed to obtain a good approximation. A consequence of using a large GMM is that its ...