This paper focuses on the automatic determination of the age of children in preschool and primary school age. For each child a Gaussian Mixture Model (GMM) is trained. As training method the Maximum A Posteriori adaptation (MAP) is used. MAP derives the speaker models from a Universal Background Model (UBM) and does not perform an independent parameter estimation. The means of each GMM are extr...

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...

In this paper, we present a speech enhancement technique based on the ambient noise classification that incorporates the Gaussian mixture model (GMM). The principal parameters of the statistical modelbased speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method and the long-term smoothing parameter of the noise estimation, are set according to the class...

In this paper, we consider a spatial-autoregressive model with autoregressive disturbances, where we allow for endogenous regressors in addition to a spatial lag of the dependent variable. We suggest a two-step generalized method of moments (GMM) and instrumental variable (IV) estimation approach extending earlier work by, e.g., Kelejian and Prucha (1998, 1999). In contrast to those papers, we ...

In an effort to increase the naturalness of concatenative speech synthesis, large speech databases may be recorded. While it is desirable to have varied prosodic and spectral characteristics in the database, it is not desirable to have variable voice quality. In this paper we present an automatic method for voice quality assessment and correction, whenever necessary, of large speech databases f...

Speaker recognition systems frequently use GMM-MAP method for modeling speakers. This method represents the speaker using a Gaussian mixture. However, in this mixture not all Gaussian components are truly representative of the speaker. In order to remove the model redundancy, this work proposes a Gaussian selection method to achieve a new GMM model only with the more representative Gaussian com...

Speaker verification (SV) systems that employ maximum a posteriori (MAP) adaptation of a Gaussian mixture model (GMM) universal background model (UBM) incur a significant teststage computational load in the calculation of a posteriori probabilities and sufficient statistics. We propose a multi-layered hash system employing a tree-structured GMM which uses Runnalls’ GMM reduction technique. The ...

We aim at developing a high-performance system using Gaussian-mixture-model (GMM) supervectors and tree-structured GMMs [6, 7, 8] for the semantic indexing task [1, 2, 3, 4]. GMM supervectors corresponding to six types of audio and visual features are extracted from video shots. Tree-structured GMMs reduce the computational cost of maximum a posteriori (MAP) adaptation for estimating GMM parame...

This paper considers time series Generalized Method of Moments (GMM) models where a subset of the parameters are time varying. We focus on an empirically relevant case with moderately large instabilities, which are well approximated by a local asymptotic embedding that does not allow the instability to be detected with certainty, even in the limit. We show that for many forms of the instability...

Background modeling is one of the key steps in any visual surveillance system. A good background modeling algorithm should be able to detect objects/targets under any environmental condition. The influence of illumination variance has been a major challenge in many background modeling algorithms. These algorithms produce poor object segmentation or consume substantial amount of computational ti...