To realize a robust spoken dialogue system for use in a real environment, the robust rejection of unintended inputs such as laughter, coughing, background speech and other noise based on GMM is implemented and examined on the basis of actual utterances. All the triggered inputs to a speech-oriented guidance system from 125 days of field tests in a public space are collected, and the occurrence ...

The ability to monitor cognitive load level in real time is extremely useful for preventing fatal operating errors or improving the efficiency of task execution. In top of the success of our previously proposed speech based cognitive load monitoring system, we explored alternative classification techniques in this paper, including simple linear kernel Support Vector Machine (SVM), hybrid SVM-GM...

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 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 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...

Gaussian mixture modeling with universal background model (GMM-UBM) is a widely used method for speaker identification, where the GMM model is used to characterize a specific speaker’s voice. The estimation of model parameters is generally performed based on the maximum likelihood (ML) or maximum a posteriori (MAP) criteria. In this way, interspeaker information that discriminates between diffe...

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...

In a general classification, the economy of any country is divided into two parts of official and invisible economies. Invisible activities drop outside the scope of the law and official economy and strongly affect socioeconomic development and the formal sector of all countries.These activities which are known under various titles including the shadow economy are influenced by various factors....

in a general classification, the economy of any country is divided into two parts of official and invisible economies. invisible activities drop outside the scope of the law and official economy and strongly affect socioeconomic development and the formal sector of all countries.these activities which are known under various titles including the shadow economy are influenced by various factors....

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 ...