In this paper, we introduce a new approach to dialect recognition which relies on the hypothesis that certain phones are realized differently across dialects. Given a speaker’s utterance, we first obtain the most likely phone sequence using a phone recognizer. We then extract GMM Supervectors for each phone instance. Using these vectors, we design a kernel function that computes the similaritie...

Voice activity detection, i.e., discrimination of the speech/nonspeech segments in a speech signal, is an important enabling technology for a variety of speech-based applications including the speaker recognition. In this work we provide a performance evaluation of the following supervised and unsupervised VAD algorithms in the context of text-dependent speaker recognition on the RSR2015 (Robus...

This paper determines the properties of standard generalized method of moments (GMM) estimators, tests, and confidence sets (CSs) in moment condition models in which some parameters are unidentified or weakly identified in part of the parameter space. The asymptotic distributions of GMM estimators are established under a full range of drifting sequences of true parameters and distributions. The...

Gaussian Mixture Modeling (GMM) is a parametric method for high dimensional density estimation. Incremental learning of GMM is very important in problems such as clustering of streaming data and robot localization in dynamic environments. Traditional GMM estimation algorithms like EM Clustering tend to be computationally very intensive in these scenarios. We present an incremental GMM estimatio...

The system GMM estimator developed by Blundell and Bond (1998) for dynamic panel data models has been widely used in empirical work; however, it does not perform well with weak instruments. This paper proposes a variation on the system GMM estimator, based on a simple transformation of the dependent variable. Simulation results indicate that, in finite samples, this transformed system GMM estim...

This paper presents results on age-group identification (AgeID) for children’s speech, using the OGI Kids corpus and GMM-UBM, GMM-SVM and i-vector systems. Regions of the spectrum containing important age information for children are identified by conducting Age-ID experiments over 21 frequency sub-bands. Results show that the frequencies above 5.5 kHz are least useful for Age-ID. The effect of...

This paper describes the Loquendo – Politecnico di Torino system evaluated on the 2006 NIST speaker recognition evaluation dataset. This system was among the best participants in this evaluation. It combines the results of two independent GMM systems: a Phonetic GMM and a classical GMM. Both systems rely on an intersession variation compensation approach, performed in the feature domain. It all...

This paper describes a multiresolutional Gaussian mixture model (GMM) for precise and stable foreground segmentation. A multiple block sizes GMM and a computationally efficient fine-to-coarse strategy, which are carried out in the Walsh transform (WT) domain, are newly introduced to the GMM scheme. By using a set of variable size block-based GMMs, a precise and stable processing is realized. Ou...

Discriminatively trained support vector machines have recently been introduced as a novel approach to speaker recognition. Support vector machines (SVMs) have a distinctly different modeling strategy in the speaker recognition problem. The standard Gaussian mixture model (GMM) approach focuses on modeling the probability density of the speaker and the background (a generative approach). In cont...

This study combines a Gaussian mixture model support vector machine (GMM-SVM) system with a nonlinear feature transformation, discriminatively trained to extract speaker specific features from MFCCs. Separation of the speaker information component and non-speaker related information in the speech signal is accomplished using a regularized siamese deep network (RSDN). RSDN learns a hidden repres...