In this paper, methods of Gaussian Mixture Model (GMM) are presented for both silence/voiced/voiceless segmentation and tone decision in Mandarin continuous speech recognition system. GMM has been used for silence/voiced/voiceless segmentation before, but the feature parameters can be modified to improve both accuracy and speed. As a popular method in pattern recognition, GMM is first proposed ...

Despite all advances in the speaker recognition domain, Gaussian Mixture Models (GMM) remain the state-of-the-art modeling technique in speaker recognition systems. The key idea is to approximate the probability density function ( ) of the feature vectors associated to a speaker with a weighted sum of Gaussian densities. Although the extremely efficient Expectation-Maximization (EM) algorithm c...

Gaussian mixture models are a very successful method for modeling the output distribution of a state in a hidden Markov model (HMM). However, this approach is limited by the assumption that the dynamics of speech features are linear and can be modeled with static features and their derivatives. In this paper, a nonlinear mixture autoregressive model is used to model state output distributions (...

acoustic analysis is a proper method in vocal fold pathology diagnosis so that itcan complement and in some cases replace the other invasive, based on direct vocalfold observation, methods. there are different approaches and algorithms for vocalfold pathology diagnosis. these algorithms usually have three stages which arefeature extraction, feature reduction and classification. in this paper in...

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 ...

In this short document, we derive a tree-independent single-tree algorithm for Gaussian mixture model training, based on a technique proposed by Moore [8]. Here, the solution we provide is tree-independent and thus will work with any type of tree and any type of traversal; this is more general than Moore’s original formulation, which was limited to mrkd-trees. This allows us to develop a flexib...

We propose a Gaussian mixture model (GMM)-based approach to discriminate stationary humans from their ghosts and clutter in through-the-wall radar images. More specifically, we use a mixture of Gaussian distributions to model the image intensity histograms corresponding to target and ghost/clutter regions. The mixture parameters, namely the means, variances, and weights of the component distrib...

In statistical methods, such as statistical static timing analysis, Gaussian mixture model (GMM) is a useful tool for representing a non-Gaussian distribution and handling correlation easily. In order to repeat various statistical operations such as summation and maximum for GMMs efficiently, the number of components should be restricted around two. In this paper, we propose a method for reduci...

Gaussian mixture model (GMM) techniques are popular for speaker identification. Theoretically, each Gaussian function should have a full covariance matrix. However, the diagonal covariance matrix is usually used because the inverse of diagonal covariance matrix can be easily calculated via expectation maximization (EM) algorithm. This paper proposes a new probabilistic principal component analy...

In this paper, we improve our previous cluster model selection method for agglomerative hierarchical speaker clustering (AHSC) based on incremental Gaussian mixture models (iGMMs). In the previous work, we measured the likelihood of all the data points in a given cluster for each mixture component of the GMM modeling the cluster. Then, we selected the N -best component Gaussians with the highes...