Variability in speech due to dialect is a major factor limiting speech system performance for speech recognition, spoken document retrieval, and dialog systems. In this study, we propose a novel discriminative algorithm to improve dialect classification for unsupervised spontaneous speech in Arabic. No transcripts are used for either training or testing, and all data are spontaneous speech. The...

This study is mainly focusing on the problem of spacecraft close-range proximity with obstacle avoidance in presence complex shape. A novel Gaussian mixture model–based nonsingular terminal sliding mode control (GMM-NTSMC) proposed. achieved by developing GMM-based potential function a switching surface NTSMC. It theoretically proved that closed-loop system globally stable. The main contributio...

In order to cluster or partition data, we often use Expectation-and-Maximization (EM) or Variational approximation with a Gaussian Mixture Model (GMM), which is a parametric probability density function represented as a weighted sum of K̂ Gaussian component densities. However, model selection to find underlying K̂ is one of the key concerns in GMM clustering, since we can obtain the desired clust...

In this paper, a new discriminative likelihood score weighting technique is proposed for speaker identification. The proposed method employs a discriminative weighting of frame-level log-likelihood scores with acoustic-phonetic classification in the Gaussian mixture model (GMM)-based speaker identification. Experiments performed on the Aurora noise-corrupted TIMIT database showed that the propo...

Clustering mixed data presents numerous challenges inherent to the very heterogeneous nature of variables. A clustering algorithm should be able, despite this heterogeneity, extract discriminant pieces information from variables in order design groups. In work we introduce a multilayer architecture model-based method called Mixed Deep Gaussian Mixture Model that can viewed as an automatic way m...

In this paper, we propose a novel frequency-domain approach to double-talk detection (DTD) based on the Gaussian mixture model (GMM). In contrast to a previous approach based on a simple and heuristic decision rule utilizing time-domain crosscorrelations, GMM is applied to a set of feature vectors extracted from the frequency-domain cross-correlation coefficients. Performance of the proposed ap...

Standard Gaussian mixture modelling does not possess time sequence information (TSI) other than that which might be embedded in the acoustic features. Dynamic time warping relates directly to TSI, time-warping two sequences of features into alignment. Here, a hybrid system embedding DTW into a GMM is presented. Improved automatic speaker verification performance is demonstrated. Testing 1000 sp...

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 ...

In this paper, a discriminative training procedure for a Gaussian Mixture Model (GMM) language identification system is described. The proposal is based on the Generalized Probabilistic Descent (GPD) algorithm and Minimum Classification Error Rates formulated to estimate the GMM parameters. The evaluation is conducted using the OGI multi-language telephone speech corpus. The experimental result...

We present a combination of an extended vector quantization (VQ) algorithm for training a speaker model and a gaussian interpretation of the VQ speaker model in the veri cation phase. This leads to a large decrease of the error rates compared to normal vector quantization and only a slight deterioration compared to full Gaussian mixture model (GMM) training. The training costs of the new method...