Design of mixture of GMMs for Query-by-Example Spoken Term Detection

Madhavi, Maulik C.; Patil, Hemant A.

doi:10.1016/j.csl.2018.04.006

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…The above studies have contributed to the optimization of SLAM data association algorithm and provided theoretical basis. A new optimized data association algorithm based on Gaussian mixture model [20], [21], [24], [25] is proposed to solve the problem of false association, complicated calculation problem in the current SLAM data association algorithm. Firstly, the local association strategy is adopted to limit data association in local region, so as to reduce the number of features involved in data association at the current moment.…”

Section: Introductionmentioning

confidence: 99%

Optimized Data Association Based on Gaussian Mixture Model

et al. 2020

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Data association is the foundation of state estimation in mobile robot simultaneous localization and mapping. Aiming at the problems of false association, high computational complexity in joint compatible branch and bound algorithm, we propose an optimized joint compatible branch and bound data association algorithm based on Gaussian mixture clustering. Firstly, the local association strategy is adopted to limit data association in local region, so as to reduce the number of features involved in data association at the current moment. Secondly, the Gaussian mixture clustering algorithm is used in local areas to group the observed values at the current moment, so as to get several groups that have little correlation with each other. Finally, joint compatible branch and bound data association algorithm is used in each group for data association, and the optimal solution is obtained according to mutual exclusion criteria and optimal criteria. The experiment results verify that the algorithm improved the accuracy of data association, reduced the computational complexity and improved the efficiency of data association. INDEX TERMS Artificial intelligence, autonomous agents, clustering algorithms, intelligent robots, simultaneous localization and mapping.

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Section: Introductionmentioning

confidence: 99%

Optimized Data Association Based on Gaussian Mixture Model

et al. 2020

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Statistical language models for query-by-example spoken document retrieval

López-Otero

Parapar

Barreiro

2020

Multimed Tools Appl

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A hybrid k-means-GMM machine learning technique for turbomachinery condition monitoring

et al. 2019

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Industrial practise typically applies pre-set original equipment manufacturers (OEMs) limits to turbomachinery online condition monitoring. However, aforementioned technique which considers sensor readings within range as normal state often get overlooked in the developments of degradation process. Thus, turbomachinery application in dire need of a responsive monitoring analysis in order to avoid machine breakdown before leading to a more disastrous event. A feasible machine learning algorithm consists of k-means and Gaussian Mixture Model (GMM) is proposed to observe the existence of signal trend or anomaly over machine active period. The aim of the unsupervised k-means is to determine the number of clusters, k according to the total trend detected from the processed dataset. Next, the designated k is input into the supervised GMM algorithm to initialize the number of components. Experiment results showed that the k-means-GMM model set up not only capable of statistically define machine state conditions, but also yield a time-dependent clustering image in reflecting degradation severity, as a mean to achieve predictive maintenance.

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Design of mixture of GMMs for Query-by-Example Spoken Term Detection

Cited by 4 publications

References 38 publications

Optimized Data Association Based on Gaussian Mixture Model

Optimized Data Association Based on Gaussian Mixture Model

Statistical language models for query-by-example spoken document retrieval

A hybrid k-means-GMM machine learning technique for turbomachinery condition monitoring

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