The primary focus of this article is the provision of tests for additional conditional moment constraints in cross-section or short panel data contexts. The principal contribution is the explicit incorporation of conditional moment restrictions defining the maintained hypothesis in the formulation of the test statistics thus mirroring that of the classical parametric likelihood setting by defin...

Audio classification has applications in a variety of contexts, such as automatic sound analysis, supervised audio segmentation and in audio information search and retrieval. Extended Baum-Welch (EBW) transformations are most commonly used as a discriminative technique for estimating parameters of Gaussian mixtures, though recently they have been applied in unsupervised audio segmentation. In t...

This paper is in the line of the recent literature on weak instruments, which, following the seminal approach of Staiger and Stock (1997) and Stock and Wright (2000) captures weak identification by drifting population moment conditions. In contrast with most of the existing literature, we do not specify a priori which parameters are strongly or weakly identified. We rather consider that weaknes...

In this paper, the change detection of Multi-Spectral (MS) remote sensing images is treated as an image segmentation issue. An unsupervised method integrating histogram-based thresholding and image segmentation techniques is proposed. In order to overcome the poor performance of thresholding techniques for strongly overlapped change/non-change signals, a Gaussian Mixture Model (GMM) with three ...

This work proposes a method of predicting pitch and voicing from mel-frequency cepstral coefficient (MFCC) vectors. Two maximum a posteriori (MAP) methods are considered. The first models the joint distribution of the MFCC vector and pitch using a Gaussian mixture model (GMM) while the second method also models the temporal correlation of the pitch contour using a combined hidden Markov model (...

This paper describes a new framework for designing speaker recognition systems based on the discriminative feature extraction (DFE) method. We apply a mel-cepstral estimation technique to the feature extractor in a Gaussian mixture model (GMM)-based text-independent speaker identification system. The mel-cepstral estimation technique uses the second-order all-pass warping function for frequency...

This paper provides a novel method to classify spoken utterances into reading style or spontaneous style. Read/spontaneous speech classification is important for extracting data to train acoustic models for speech recognition from real data in which read speech and spontaneous speech samples are mixed. We analyzed 23,900 reading and 31,988 spontaneous utterances of 30 speakers and found that va...

Statistic classifiers operate on features that generally include both, useful and useless information. These two types of information are difficult to separate in feature domain. Recently, a new paradigm based on Factor Analysis (FA) proposed a model decomposition into useful and useless components. This method has successfully been applied to speaker recognition tasks. In this paper, we study ...

To model the speech utterance at a finer granularity, this paper presents a novel state-alignment based supervector modeling method for text-independent speaker verification, which takes advantage of state-alignment method used in hidden Markov model (HMM) based acoustic modeling in speech recognition. By this way, the proposed modeling method can convert a text-independent speaker verification...

Natural and synthetic steroid hormones excreted into the environment are potentially threatening the population dynamics of all kinds of animals and public health. We have previously isolated a steroid degrading bacterial strain (H5) from the Baltic Sea, at Kiel, Germany. 16S-rRNA analysis showed that bacterial strain H5 belongs to the genus Vibrio, family Vibrionaceae and class Gamma-Proteobac...