We proposed to use support vector machines (SVMs) to recognize speakers from signal transcoded with different speech codecs. Experiments with SVM-based text-independent speaker classification using a linear GMM supervector kernel were presented for six different codecs and uncoded speech. Both matched (the same codec for creating speaker models and for testing) and mismatched conditions were in...

This paper exploits the novelty detection technique towards identifying hazardous situations. The proposed system elaborates on the audio part of the PROMETHEUS database which includes heterogeneous recordings and was captured under real-world conditions. Three types of environments were used: smart-home, indoors public space and outdoors public space. The multidomain set of descriptors was for...

This paper considers the identi…cation and estimation of network models with agents interacting in multiple activities. We establish the model identi…cation using both linear and quadratic moment conditions. The quadratic moment conditions exploit the covariance structure of individuals’choices in the same and related activities, and facilitate the identi…cation of peer e¤ects when exclusion re...

This work proposes a system for acoustic event classification using signals acquired by a Distributed Microphone Network (DMN). The system is based on the combination of Gaussian Mixture Models (GMM) and Support Vector Machines (SVM). The acoustic event list includes both speech and non-speech events typical of seminars and meetings. The robustness of the system was investigated by considering ...

In our previous work, we have proposed a speaker modeling technique using spectral and pitch features for text-independent speaker identification based on Multi-Space Probability Distribution Gaussian Mixture Models (MSD-GMMs). We have presented a maximum likelihood (ML) estimation procedure for the MSD-GMM parameters and demonstrated its high recognition performance. In this paper, we describe...

This paper discusses likelihood-based estimation of panel data models with individualspecific effects and both lagged dependent variable regressors and additional predetermined explanatory variables. The resulting new estimator, labeled as sub-system LIML (ssLIML), is asymptotically equivalent to standard panel GMM as N →∞ for fixed T , but tends to present smaller biases in finite samples as i...

Using i-vector space features has been shown to be very successful in speaker and language identification. In this paper, we evaluate using the i-vector framework for emotion recognition from speech. Instead of using standard i-vector features, we propose to use concatenated emotion specific i-vector features. For each emotion category, a GMM supervector is generated via adaptation of the neura...

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 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...

Background subtraction methods are widely exploited for moving object detection in many applications. A key issue to these methods is how to model and maintain the background correctly and efficiently. This paper describes a foreground detector used in our surveillance system characterized by multiple Gaussian statistics. Compared with the existing methods, our Gaussian mixture model (GMM) diff...