In this paper, we adopt the boosting framework to improve the performance of acoustic-based Gaussian mixture model (GMM) Language Identification (LID) systems. We introduce a set of low-complexity, boosted target and anti-models that are estimated from training data to improve class separation, and these models are integrated during the LID backend process. This results in a fast estimation pro...

As machine learning techniques become more popular and computers become capable of storing and processing large quantities of data, there have been many recent efforts to incorporate such techniques into structural econometric models. My research aims to extend this literature by introducing the techniques of regularization and classification (from machine learning) into Generalized Method of M...

Among the applications of a radar system, target classification for ground surveillance is one of the most widely used. This paper deals with micro-Doppler Signature (μ-DS) based radar Automatic Target Recognition (ATR). The main goal for performing μ-DS classification using speech processing tools was to investigate whether automatic speech recognition (ASR) techniques are suitable methods for...

For noise-robust automatic speech recognition (ASR), we propose a novel voice activity detection (VAD) method based on a combination of multiple features. The scheme uses a weighted combination of four conventional VAD features: amplitude level, zero crossing rate, spectral information, and Gaussian mixture model (GMM) likelihood. The weights for combination are adaptively updated using minimum...

In this letter, an acoustic environment classification algorithm based on the 3GPP2 selectable mode vocoder (SMV) is proposed for context-aware mobile phones. Classification of the acoustic environment is performed based on a Gaussian mixture model (GMM) using coding parameters of the SMV extracted directly from the encoding process of the acoustic input data in the mobile phone. Experimental r...

This paper derives consistent standard errors for a panel Tobit model in the presence of correlated errors. The problem is framed in the context of Newey and West (1987), considering the Tobit model as a special case of a GMM estimator. JEL codes: C23, C24

This paper presents a speaker identification system based on Gaussian Mixture Models (GMM) using the variational Bayesian method. Maximum Likelihood (ML) and Maximum A Posterior (MAP) are well-known methods for estimating GMM parameters. However, the overtraining problem occurs with insufficient data due to a point estimate of model parameters. The Bayesian approach estimates a posterior distri...

An automated method of MR brain image segmentation is presented. A block based Expectation Maximization Algorithm is proposed for the tissue classification of MR brain images. The standard Gaussian Mixture Model is the most widely used method for MR Brain image segmentation and Expectation Maximization algorithm is used to estimate the model parameters. The Gaussian Mixture Model considers each...

This article analyzes and compares influence of different types of spectral and prosodic features for Czech and Slovak emotional speech classification based on Gaussian mixture models (GMM). Influence of initial setting of parameters (number of mixture components and used number of iterations) for GMM training process was analyzed, too. Subsequently, analysis was performed to find how correctne...

Quantifying how the spectral content of speech relates to changes in mental state may be crucial in building an objective speech-based depression classification system with clinical utility. This paper investigates the hypothesis that important depression based information can be captured within the covariance structure of a Gaussian Mixture Model (GMM) of recorded speech. Significant negative ...