We released genetically modified Pseudomonas putida WCS358r into the rhizospheres of wheat plants. The two genetically modified derivatives, genetically modified microorganism (GMM) 2 and GMM 8, carried the phz biosynthetic gene locus of strain P. fluorescens 2-79 and constitutively produced the antifungal compound phenazine-1-carboxylic acid (PCA). In the springs of 1997 and 1998 we sowed whea...

To obtain consistency and asymptotic normality, a generalized method of moments (GMM) estimator typically is defined to be an approximate global minimizer of a GMM criterion function. To compute such an estimator, however, can be problematic because of the difficulty of global optimization. In consequence, practitioners usually ignore the problem and take the GMM estimator to be the result of a...

Most techniques for speaker verification today use Gaussian Mixture Models (GMMs) and make the decision by comparing the likelihood of the speaker model to the likelihood of a universal background model (UBM). The paper proposes to replace the UBM by an individual background model (IBM) that is generated for each speaker. The IBM is created using the K-nearest cohort models and the UBM by a sim...

This paper presents a unified i-vector framework for language identification (LID) based on deep bottleneck networks (DBN) trained for automatic speech recognition (ASR). The framework covers both front-end feature extraction and back-end modeling stages.The output from different layers of a DBN are exploited to improve the effectiveness of the i-vector representation through incorporating a mi...

Feature selection is a critical step for pattern recognition and many other applications. Typically, feature selection strategies can be categorized into wrapper and filter approaches. Filter approach has attracted much attention because of its flexibility and computational efficiency. Previously, we have developed an ICA-MI framework for feature selection, in which the Mutual Information (MI) ...

Large vocabulary continuous speech recognition systems are known to be computationally intensive. A major bottleneck is the Gaussian mixture model (GMM) computation and various techniques have been proposed to address this problem. We present a systematic study of fast GMM computation techniques. As there are a large number of these and it is impractical to exhaustively evaluate all of them, we...

Missing data is one of the most common challenges facing empirical researchers. This paper presents a general GMM framework for dealing with missing data on explanatory variables or instrumental variables. For a linear-regression model with missing covariate data, an efficient GMM estimator under minimal assumptions on missingness is proposed. The estimator, which also allows for a specificatio...

This paper proposes a method of predicting the formant frequencies of a frame of speech from its mel-frequency cepstral coefficient (MFCC) representation. Prediction is achieved through the creation of a Gaussian mixture model (GMM) which models the joint density of formant frequencies and MFCCs. Using this GMM and an input MFCC vector, a maximum a posteriori (MAP) prediction of the formant fre...