Results 201 to 210 of about 33,807 (240)
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2007 IEEE International Conference on Signal Processing and Communications, 2007
In this paper the application of Gaussian mixture model (GMM) classifier is investigated as an efficient post-processing method to enhance the performance of GMM-based speaker identification systems; such as Gaussian mixture model universal background model (GMM-UBM) scheme.
H. R. Sadegh Mohammadi, R. Saeidi
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In this paper the application of Gaussian mixture model (GMM) classifier is investigated as an efficient post-processing method to enhance the performance of GMM-based speaker identification systems; such as Gaussian mixture model universal background model (GMM-UBM) scheme.
H. R. Sadegh Mohammadi, R. Saeidi
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Gaussian Mixture Model (GMM) Based Object Detection and Tracking using Dynamic Patch Estimation
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019In this paper, we have developed a Gaussian Mixture Model (GMM) based algorithm with dynamic patch estimation for real-time detection and tracking of a known object. This research work detects the object of interest, estimates its 3-D position using Extended Kalman Filter (EKF) and generates the control output to the quad-rotor to track the target. The
Vishnu Anand +3 more
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Unsupervised Learning of Finite Gaussian Mixture Models (GMMs): A Greedy Approach
2011In this work we propose a clustering algorithm that learns on-line a finite gaussian mixture model from multivariate data based on the expectation maximization approach. The convergence of the right number of components as well as their means and covariances is achieved without requiring any careful initialization.
Nicola Greggio +2 more
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International Journal of Pattern Recognition and Artificial Intelligence, 2014
For solving speaker identification problems, the approach proposed by Reynolds [IEEE Signal Process. Lett.2 (1995) 46–48], using Gaussian Mixture Models (GMMs) based on Mel Frequency Cepstral Coefficients (MFCCs) as features, is one of the most effective available in the literature.
AMITA PAL +3 more
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For solving speaker identification problems, the approach proposed by Reynolds [IEEE Signal Process. Lett.2 (1995) 46–48], using Gaussian Mixture Models (GMMs) based on Mel Frequency Cepstral Coefficients (MFCCs) as features, is one of the most effective available in the literature.
AMITA PAL +3 more
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An Adaptive Segmentation Method Based on Gaussian Mixture Model (GMM) Clustering for DNA Microarray
2014 International Conference on Intelligent Computing Applications, 2014Microarray allows us to efficiently analyse valuable gene expression data. In this paper we propose a effective methodology for analysis of microarrays. Earlier a new gridding algorithm is proposed to address all individual spots and to determine their borders.
M. Parthasarathy, R. Ramya, A. Vijaya
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2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006
In this paper a Gaussian mixture model (GMM) classifier, called GMM identifier, is proposed as an efficient post-processing method to enhance the performance of a CMM-based speaker verification system; such as Gaussian mixture model universal background model (GMM-UBM) and structural Gaussian mixture models with structural background model (SGMM-SBM ...
R. Saeidi +2 more
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In this paper a Gaussian mixture model (GMM) classifier, called GMM identifier, is proposed as an efficient post-processing method to enhance the performance of a CMM-based speaker verification system; such as Gaussian mixture model universal background model (GMM-UBM) and structural Gaussian mixture models with structural background model (SGMM-SBM ...
R. Saeidi +2 more
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Build a Model for Speech Emotion Recognition using Gaussian Mixture Model (GMM)
2023 2nd International Conference on Futuristic Technologies (INCOFT), 2023Tirumalasetti Teja Sri +3 more
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Parameter estimation of Gaussian mixture models (GMM) with expectation maximization (EM) algorithm
AIP Conference Proceedings, 2022Wardatul Jannah, Dewi R. S. Saputro
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Speech Emotion Recognition using Gaussian Mixture Model (GMM) and K-Nearest Neighbors (KNN)
This paper aimed to propose a novel methodology to improve the accuracy and efficiency of speech emotion recognition, through to the multilingual setting. The paper’s topic was the low precision obtained by the systems for speech emotion recognition, especially in multilingual settings.Kirtika Iyer +3 more
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Gaussian Process Regression for Materials and Molecules
Chemical Reviews, 2021Volker L Deringer +2 more
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