In earlier studies, we employed a large prosodic feature vector to assess the quality of L2 learner’s utterances with respect to sentence melody and rhythm. In this paper, we combine these features with two standard approaches in paralinguistic analysis: (1) features derived from a Gaussian Mixture Model used as Universal Background Model (GMM-UBM), and (2) openSMILE, an open-source toolkit for...

We propose an algorithm that allows online training of a context dependent DNN model. It designs a state inventory based on DNN features and jointly optimizes the DNN parameters and alignment of the training data. The process allows flat starting a model from scratch and avoids any dependency on a GMM acoustic model to bootstrap the training process. A 15k state model trained with the proposed ...

We generalize the classical max-min rate allocation policy with the support of the minimum rate requirement and peak rate constraint for each connection. Since a centralized algorithm for the generalized maxmin (GMM) rate allocation requires global information, which is di cult to maintain and manage in a large network, we develop a distributed protocol to achieve the GMM policy using the avail...

In this paper, we describe systems that were developed for the Open Performance Sub-Challenge of the INTERSPEECH 2009 Emotion Challenge. We participate in both two-class and fiveclass emotion detection. For the two-class problem, the best performance is obtained by logistic regression fusion of three systems. These systems use shortand long-term speech features. Fusion allowed to an absolute im...

This paper establishes the almost sure convergence and asymptotic normality of levels and differenced quasi maximum-likelihood (QML) estimators of dynamic panel data models. The QML estimators are robust with respect to initial conditions, conditional and time-series heteroskedasticity, and misspecification of the log-likelihood. The paper also provides an ECME algorithm for calculating levels ...

A spectral conversion method using multiple Gaussian Mixture Models (GMMs) based on the Bayesian framework is proposed. A typical spectral conversion framework is based on a GMM. However, in this conventional method, a GMM-appropriate number of mixtures is dependent on the amount of training data, and thus the number of mixtures should be determined beforehand. In the proposed method, the varia...

Gaussian Mixture Model (GMM) computation is known to be one of the most computation-intensive components of speech recognition. In our previous work, context-independent model based GMM selection (CIGMMS) was found to be an effective way to reduce the cost of GMM computation without significant loss in recognition accuracy. In this work, we propose three methods to further improve the performan...

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 ...

To reduce the high dimensionality required for training of feature vectors in speaker identification, we propose an efficient GMM based on local PCA with fuzzy clustering. The proposed method firstly partitions the data space into several disjoint clusters by fuzzy clustering, and then performs PCA using the fuzzy covariance matrix on each cluster. Finally, the GMM for speaker is obtained from ...

The chapter discusses generalized method of moments (GMM) estimation methods for spatial models. Much of the discussion is on GMM estimation of Cliff-Ord-type models where spatial interactions are modeled in terms of spatial lags. The chapter also discusses recent developments on GMM estimation from data processes which are spatially α-mixing. I.R. Prucha Department of Economics, University of ...