IEEE Organizations related to Maximum Likelihood Linear Regression

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Conferences related to Maximum Likelihood Linear Regression

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2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

FUZZ-IEEE 2021 will represent a unique meeting point for scientists and engineers, both from academia and industry, to interact and discuss the latest enhancements and innovations in the field. The topics of the conference will cover all the aspects of theory and applications of fuzzy sets, fuzzy logic and associated approaches (e.g. aggregation operators such as the Fuzzy Integral), as well as their hybridizations with other artificial and computational intelligence techniques.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Multimedia and Expo (ICME)

Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.

  • 2019 IEEE International Conference on Multimedia and Expo (ICME)

    speech, audio, image, video, text and new sensor signal processingsignal processing for media integration3D imaging, visualization and animationvirtual reality and augmented realitymulti-modal multimedia computing systems and human-machine interactionmultimedia communications and networkingmedia content analysis and searchmultimedia quality assessmentmultimedia security and content protectionmultimedia applications and servicesmultimedia standards and related issues

  • 2018 IEEE International Conference on Multimedia and Expo (ICME)

    The IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities. ICME also features an Exposition of multimedia products and prototypes.

  • 2017 IEEE International Conference on Multimedia and Expo (ICME)

    Topics of interest include, but are not limited to: – Speech, audio, image, video, text and new sensor signal processing – Signal processing for media integration – 3D visualization and animation – 3D imaging and 3DTV – Virtual reality and augmented reality – Multi-modal multimedia computing systems and human-machine interaction – Multimedia communications and networking – Media content analysis – Multimedia quality assessment – Multimedia security and content protection – Multimedia databases and digital libraries – Multimedia applications and services – Multimedia standards and related issues

  • 2016 IEEE International Conference on Multimedia and Expo (ICME)

    Topics of interest include, but are not limited to:- Speech, audio, image, video, text and new sensor signal processing- Signal processing for media integration- 3D visualization and animation- 3D imaging and 3DTV- Virtual reality and augmented reality- Multi-modal multimedia computing systems and human-machine interaction- Multimedia communications and networking- Media content analysis- Multimedia quality assessment- Multimedia security and content protection- Multimedia databases and digital libraries- Multimedia applications and services- Multimedia standards and related issues

  • 2015 IEEE International Conference on Multimedia and Expo (ICME)

    With around 1000 submissions and 500 participants each year, the IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2014 IEEE International Conference on Multimedia and Expo (ICME)

    The IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications. In 2014, an Exposition of multimedia products, prototypes and animations will be held in conjunction with the conference.Topics of interest include, but are not limited to:

  • 2013 IEEE International Conference on Multimedia and Expo (ICME)

    To promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2012 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE Societies. It exchanges the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2011 IEEE International Conference on Multimedia and Expo (ICME)

    Speech, audio, image, video, text processing Signal processing for media integration 3D visualization, animation and virtual reality Multi-modal multimedia computing systems and human-machine interaction Multimedia communications and networking Multimedia security and privacy Multimedia databases and digital libraries Multimedia applications and services Media content analysis and search Hardware and software for multimedia systems Multimedia standards and related issues Multimedia qu

  • 2010 IEEE International Conference on Multimedia and Expo (ICME)

    A flagship multimedia conference sponsored by four IEEE societies, ICME serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2009 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo is a major annual international conference with the objective of bringing together researchers, developers, and practitioners from academia and industry working in all areas of multimedia. ICME serves as a forum for the dissemination of state-of-the-art research, development, and implementations of multimedia systems, technologies and applications.

  • 2008 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo is a major annual international conference with the objective of bringing together researchers, developers, and practitioners from academia and industry working in all areas of multimedia. ICME serves as a forum for the dissemination of state-of-the-art research, development, and implementations of multimedia systems, technologies and applications.

  • 2007 IEEE International Conference on Multimedia and Expo (ICME)

  • 2006 IEEE International Conference on Multimedia and Expo (ICME)

  • 2005 IEEE International Conference on Multimedia and Expo (ICME)

  • 2004 IEEE International Conference on Multimedia and Expo (ICME)

  • 2003 IEEE International Conference on Multimedia and Expo (ICME)

  • 2002 IEEE International Conference on Multimedia and Expo (ICME)

  • 2001 IEEE International Conference on Multimedia and Expo (ICME)

  • 2000 IEEE International Conference on Multimedia and Expo (ICME)


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


OCEANS 2020 - SINGAPORE

An OCEANS conference is a major forum for scientists, engineers, and end-users throughout the world to present and discuss the latest research results, ideas, developments, and applications in all areas of oceanic science and engineering. Each conference has a specific theme chosen by the conference technical program committee. All papers presented at the conference are subsequently archived in the IEEE Xplore online database. The OCEANS conference comprises a scientific program with oral and poster presentations, and a state of the art exhibition in the field of ocean engineering and marine technology. In addition, each conference can have tutorials, workshops, panel discussions, technical tours, awards ceremonies, receptions, and other professional and social activities.

  • OCEANS 2019 - Marseille

    Research, Development, and Operations pertaining to the Oceans

  • 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO)

    The conference scope is to provide a thematic umbrella for researchers working in OCEAN engineering and related fields across the world to discuss the problems and potential long term solutions that concernnot only the oceans in Asian pacific region, but the world ocean in general.

  • OCEANS 2017 - Aberdeen

    Papers on ocean technology, exhibits from ocean equipment and service suppliers, student posters and student poster competition, tutorials on ocean technology, workshops and town hall meetings on policy and governmental process.

  • OCEANS 2016 - Shanghai

    Papers on ocean technology, exhibits from ocean equipment and service suppliers, student posters and student poster competition, tutorial on ocean technology, workshops and town hall meetings on policy and governmental process.

  • OCEANS 2015 - Genova

    The Marine Technology Society and the Oceanic Engineering Society of IEEE cosponsor a joint annual conference and exposition on ocean science, engineering and policy. The OCEANS conference covers four days. One day for tutorials and three for approx. 450 technical papers and 50-200 exhibits.

  • OCEANS 2014 - TAIPEI

    The OCEANS conference covers all aspects of ocean engineering from physics aspects through development and operation of undersea vehicles and equipment.

  • OCEANS 2013 - NORWAY

    Ocean related technologies. Program includes tutorials, three days of technical papers and a concurrent exhibition. Student poster competition.

  • OCEANS 2012 - YEOSU

    The OCEANS conferences covers four days with tutorials, exhibits and three days of parallel tracks that address all aspects of oceanic engineering.

  • OCEANS 2011 - SPAIN

    All Oceans related technologies.

  • OCEANS 2010 IEEE - Sydney

  • OCEANS 2009 - EUROPE

  • OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean

  • OCEANS 2007 - EUROPE

    The theme 'Marine Challenges: Coastline to Deep Sea' focuses on the significant challenges, from the shallowest waters around our coasts to the deepest subsea trenches, that face marine, subsea and oceanic engineers in their drive to understand the complexities of the world's oceans.

  • OCEANS 2006 - ASIA PACIFIC

  • OCEANS 2005 - EUROPE


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Periodicals related to Maximum Likelihood Linear Regression

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Most published Xplore authors for Maximum Likelihood Linear Regression

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Xplore Articles related to Maximum Likelihood Linear Regression

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SVM Based Speaker Recognition Using Maximum a posteriori Linear Regression

2009 International Conference on Electronic Computer Technology, 2009

Maximum likelihood linear regression (MLLR) is a widely used technique for speaker adaptation in large vocabulary speech recognition system. Recently, using MLLR transforms as features for SVM based speaker recognition tasks has been proposed, achieving performance comparable to that obtained with cepstral features. In this paper, we focus on calculating the transforms based on a GMM universal background model (UBM). ...


Minumum generation error linear regression based model adaptation for HMM-based speech synthesis

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008

Due to the inconsistency between the maximum likelihood (ML) based training and the synthesis application in HMM-based speech synthesis, a minimum generation error (MGE) criterion had been proposed for HMM training. This paper continues to apply the MGE criterion to model adaptation for HMM-based speech synthesis. We propose a MGE linear regression (MGELR) based model adaptation algorithm, where the regression ...


Commentary Paper on “Learning and Classification of Trajectories in Dynamic Scenes: A General Framework for Live Video Analysis”

2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, 2008

The paper describes a general platform for live video analysis. The first stage of the platform is to build a topological scene description by learning the location of nodes (i.e. zones), which are called points of interest. There are two kinds of points of interest, the entry-exit zones (areas where moving object appear and disappear in the scene) and the ...


Speaker adaptation using discriminative linear regression on time-varying mean parameters in trended HMM

IEEE Signal Processing Letters, 1998

In this letter, we report our recent work on applications of the combined maximum likelihood linear regression (MLLR) and the minimum classification error training (MCE) approach to estimating the time-varying polynomial Gaussian mean functions in the trended hidden Markov model (HMM). We call this integrated approach the minimum classification error linear regression (MCELR), which has been developed and implemented in ...


Automatic generation of phonetic regression class trees for MLLR adaptation

IEEE Transactions on Speech and Audio Processing, 2001

In this paper, it is shown that a correlation criterion is the appropriate criterion for bottom-up clustering to obtain broad phonetic class regression trees for maximum likelihood linear regression (MLLR)-based speaker adaptation. The correlation structure among speech units is estimated on the speaker-independent training data. In adaptation experiments the tree outperformed a regression tree obtained from clustering according to closeness ...


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Educational Resources on Maximum Likelihood Linear Regression

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IEEE-USA E-Books

  • SVM Based Speaker Recognition Using Maximum a posteriori Linear Regression

    Maximum likelihood linear regression (MLLR) is a widely used technique for speaker adaptation in large vocabulary speech recognition system. Recently, using MLLR transforms as features for SVM based speaker recognition tasks has been proposed, achieving performance comparable to that obtained with cepstral features. In this paper, we focus on calculating the transforms based on a GMM universal background model (UBM). Rather than estimating the transforms using maximum likelihood criterion, this paper describes a new feature extraction technique for speaker recognition based on maximum a posteriori linear regression (MAPLR), which uses maximum a posteriori (MAP) as estimation criterion. We perform experiments on a NIST SRE 2008 corpus. Experimental results show that the system based on MAPLR technique outperforms MLLR in the task of speaker recognition.

  • Minumum generation error linear regression based model adaptation for HMM-based speech synthesis

    Due to the inconsistency between the maximum likelihood (ML) based training and the synthesis application in HMM-based speech synthesis, a minimum generation error (MGE) criterion had been proposed for HMM training. This paper continues to apply the MGE criterion to model adaptation for HMM-based speech synthesis. We propose a MGE linear regression (MGELR) based model adaptation algorithm, where the regression matrices used to transform source models to target models are optimized to minimize the generation errors for the input speech data uttered by the target speaker. The proposed MGELR approach was compared with the maximum likelihood linear regression (MLLR) based model adaptation. Experimental results indicate that the generation errors were reduced after the MGELR-based model adaptation. And from the subjective listening test, the discrimination and the quality of the synthesized speech using MGELR were better than the results using MLLR.

  • Commentary Paper on “Learning and Classification of Trajectories in Dynamic Scenes: A General Framework for Live Video Analysis”

    The paper describes a general platform for live video analysis. The first stage of the platform is to build a topological scene description by learning the location of nodes (i.e. zones), which are called points of interest. There are two kinds of points of interest, the entry-exit zones (areas where moving object appear and disappear in the scene) and the stopping zones (areas where the moving objects have slow speed or remain in a circle of radius R for more than t seconds). The zones are modelled by 2D Gaussian methods. The routes between nodes are learned considering only the spatial location of trajectories in the image scene and using fuzzy C means (FCM) clusterization of the trajectories that begin in an entry zone, end in an exit zone and do not remain in a stop zone. The main trajectory cluster points are aligned using dynamic time warping and merged if the Euclidean distance is lower than a threshold.The second stage consists in the modelling of the paths by introducing not only the spatial location of the trajectories but the dynamics as well to analyze behaviour. The spatio-temporal path properties are encoded using Hidden Markov Models. The platform makes one model for each cluster computed in the previous stage. The training of each HMM model is done with the paths associated to each FCM cluster. The platform adds new models by using a batch update procedure. Trajectories that do not fit in any of the models are collected and re-clustered periodically. The HMMs are updated using maximum likehood linear regression (MLLR). Each time a new trajectory is classified into a path, a transformation is learned and applied to the mean of each of the HMM states updating its corresponding path model.The last stage comprises the behaviour analysis. Each novel trajectory detected is classified into a path by comparison with all the HMMs using forward-backward procedure finding the HMM with the maximum likelihood. Anomalous trajectories are recognized deciding that its likelihood is low, by comparing the likelihood with a decision threshold. The decision threshold is learned during training. The platform also provides an online tracking analysis method. In this case a small window of the last trajectory points is analysed, this window is constantly updated with incoming points. The live tracking classification is done considering only the most recent points of the window by comparing the points with the HMMs to estimate the likelihood at each time the window is updated. The platform can detect abnormalities during live tracking by a similar method used with complete trajectories. Path prediction is described using the HMMs by calculating the top 3 best fit paths determined with the HMM likelihoods and then estimating the probability of the incomplete trajectory to remain in one of those paths.

  • Speaker adaptation using discriminative linear regression on time-varying mean parameters in trended HMM

    In this letter, we report our recent work on applications of the combined maximum likelihood linear regression (MLLR) and the minimum classification error training (MCE) approach to estimating the time-varying polynomial Gaussian mean functions in the trended hidden Markov model (HMM). We call this integrated approach the minimum classification error linear regression (MCELR), which has been developed and implemented in speaker adaptation experiments using TI46 corpora. Results show that the adaptation of linear regression on time-varying mean parameters is always better when fewer than three adaptation tokens are used.

  • Automatic generation of phonetic regression class trees for MLLR adaptation

    In this paper, it is shown that a correlation criterion is the appropriate criterion for bottom-up clustering to obtain broad phonetic class regression trees for maximum likelihood linear regression (MLLR)-based speaker adaptation. The correlation structure among speech units is estimated on the speaker-independent training data. In adaptation experiments the tree outperformed a regression tree obtained from clustering according to closeness in acoustic space and achieved results comparable with those of a manually designed broad phonetic class tree.

  • Linear regression under maximum a posteriori criterion with Markov random field prior

    Speaker adaptation using linear transformations under the maximum a posteriori (MAP) criterion has been studied in this paper. The purpose is to improve the matrix estimation in the widely used maximum likelihood linear regression (MLLR) adaptation, which might generate poorly structured transform matrices when adaptation data are sparse. Unlike traditional MAP based adaptations, many known prior distributions of HMM parameters, such as normal-Washart priors, do not have a close form solution in the transform estimation. In Markov random field linear regression (MRFLR), the prior distribution of HMM parameters is modeled by Markov random field, which leads to a close form solution of estimating the linear transforms. Experimental results show that MRFLR outperforms MLLR when adaptation data are sparse, and converges to the MLLR performances when more adaptation data are available.

  • Model Adaptation for HMM-Based Speech Synthesis under Minimum Generation Error Criterion

    In order to solve the issues related to the maximum likelihood (ML) based HMM training for HMM-based speech synthesis, a minimum generation error (MGE) criterion had been proposed. This paper continues to apply the MGE criterion to model adaptation for HMM-based speech synthesis. We introduce a MGE linear regression (MGELR) based model adaptation algorithm, where the transforms from source HMMs to target HMMs are optimized to minimize the generation errors for the adaptation data of the target speaker. The regression matrices for both mean vector and covariance matrix of Gaussian distribution are re-estimated. The proposed MGELR approach was compared with the maximum likelihood linear regression (MLLR) based model adaptation. Experimental results indicate that the generation errors were reduced after the MGELR-based model adaptation. And from the subjective listening test, the speaker similarity and the quality of the synthesized speech using MGELR were better than the results using MLLR.

  • Blind Separation and Equalization Using Novel Hill-Climbing Optimization

    In this paper, we construct a maximum-likelihood-equivalent metric or auxiliary function, which can result in a novel expectation-maximization Hill- Climbing (EM-HC) optimization procedure; it can be easily implemented for the estimation, detection and clustering applications since it is based on the simple auxiliary function. In this paper, one major application of our new EM- HC method, namely the blind separation and blind channel equalization, is presented and an efficient Iterative weighted least-mean squared (IWLMS) algorithm is derived thereupon. The new IWLMS algorithm derived from the EM-HC techniques greatly outperforms the prevalent blind equalization algorithm based on the constant-modulus criteria according to simulations.

  • Cluster adaptive training of hidden Markov models

    When performing speaker adaptation, there are two conflicting requirements. First, the speaker transform must be powerful enough to represent the speaker. Second, the transform must be quickly and easily estimated for any particular speaker. The most popular adaptation schemes have used many parameters to adapt the models to be representative of an individual speaker. This limits how rapidly the models may be adapted to a new speaker or the acoustic environment. This paper examines an adaptation scheme requiring very few parameters, cluster adaptive training (CAT). CAT may be viewed as a simple extension to speaker clustering. Rather than selecting a single cluster as representative of a particular speaker, a linear interpolation of all the cluster means is used as the mean of the particular speaker. This scheme naturally falls into an adaptive training framework. Maximum likelihood estimates of the interpolation weights are given. Furthermore, simple re- estimation formulae for cluster means, represented both explicitly and by sets of transforms of some canonical mean, are given. On a speaker-independent task CAT reduced the word error rate using very little adaptation data. In addition when combined with other adaptation schemes it gave a 5% reduction in word error rate over adapting a speaker-independent model set.

  • Gaussian Backend design for open-set language detection

    This paper proposes a new approach to the challenging open-set language detection task. Most state-of-the-art approaches make use of data sources with several out-of-set languages to model such languages. In the proposed approach, no additional data from out-ofset languages is required, only date from the target languages is used. Experiments are conducted using the LRE-05 and the LRE-07 evaluation data sets with the 30s condition. A Cavg of 4.5% and 3.4% is obtained on these data set, respectively. These results are comparable with other reported results.



Standards related to Maximum Likelihood Linear Regression

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