Conferences related to Speaker Recognition

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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.


IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society

IECON is focusing on industrial and manufacturing theory and applications of electronics, controls, communications, instrumentation and computational intelligence.


Oceans 2020 MTS/IEEE GULF COAST

To promote awareness, understanding, advancement and application of ocean engineering and marine technology. This includes all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.

  • OCEANS 2018 MTS/IEEE Charleston

    Ocean, coastal, and atmospheric science and technology advances and applications

  • OCEANS 2017 - Anchorage

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

  • OCEANS 2016

    The Marine Technology Scociety and the Oceanic Engineering Society of the IEEE cosponor 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. 500 technical papers and 150 -200 exhibits.

  • OCEANS 2015

    The Marine Technology Scociety and the Oceanic Engineering Society of the IEEE cosponor 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 150-200 exhibits.

  • OCEANS 2014

    The OCEANS conference covers four days. One day for tutorials and three for approx. 450 technical papers and 150-200 exhibits.

  • OCEANS 2013

    Three days of 8-10 tracks of technical sessions (400-450 papers) and concurent exhibition (150-250 exhibitors)

  • OCEANS 2012

    Ocean related technology. Tutorials and three days of technical sessions and exhibits. 8-12 parallel technical tracks.

  • OCEANS 2011

    The Marine Technology Society and the Oceanic Engineering Scociety of the IEEE cosponsor a joint annual conference and exposition on ocean science engineering, and policy.

  • OCEANS 2010

    The Marine Technology Society and the Oceanic Engineering Scociety of the IEEE cosponsor a joint annual conference and exposition on ocean science engineering, and policy.

  • OCEANS 2009

  • OCEANS 2008

    The Marine Technology Society (MTS) and the Oceanic Engineering Society (OES) of the Institute of Electrical and Electronic Engineers (IEEE) cosponsor a joint conference and exposition on ocean science, engineering, education, and policy. Held annually in the fall, it has become a focal point for the ocean and marine community to meet, learn, and exhibit products and services. The conference includes technical sessions, workshops, student poster sessions, job fairs, tutorials and a large exhibit.

  • OCEANS 2007

  • OCEANS 2006

  • OCEANS 2005

  • OCEANS 2004

  • OCEANS 2003

  • OCEANS 2002

  • OCEANS 2001

  • OCEANS 2000

  • OCEANS '99

  • OCEANS '98

  • OCEANS '97

  • OCEANS '96


2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Industrial Informatics, Computational Intelligence, Control and Systems, Cyber-physicalSystems, Energy and Environment, Mechatronics, Power Electronics, Signal and InformationProcessing, Network and Communication Technologies


2018 24th International Conference on Pattern Recognition (ICPR)

ICPR will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields

  • 2016 23rd International Conference on Pattern Recognition (ICPR)

    ICPR'2016 will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields.

  • 2014 22nd International Conference on Pattern Recognition (ICPR)

    ICPR 2014 will be an international forum for discussions on recent advances in the fields of Pattern Recognition; Machine Learning and Computer Vision; and on applications of these technologies in various fields.

  • 2012 21st International Conference on Pattern Recognition (ICPR)

    ICPR is the largest international conference which covers pattern recognition, computer vision, signal processing, and machine learning and their applications. This has been organized every two years by main sponsorship of IAPR, and has recently been with the technical sponsorship of IEEE-CS. The related research fields are also covered by many societies of IEEE including IEEE-CS, therefore the technical sponsorship of IEEE-CS will provide huge benefit to a lot of members of IEEE. Archiving into IEEE Xplore will also provide significant benefit to the all members of IEEE.

  • 2010 20th International Conference on Pattern Recognition (ICPR)

    ICPR 2010 will be an international forum for discussions on recent advances in the fields of Computer Vision; Pattern Recognition and Machine Learning; Signal, Speech, Image and Video Processing; Biometrics and Human Computer Interaction; Multimedia and Document Analysis, Processing and Retrieval; Medical Imaging and Visualization.

  • 2008 19th International Conferences on Pattern Recognition (ICPR)

    The ICPR 2008 will be an international forum for discussions on recent advances in the fields of Computer vision, Pattern recognition (theory, methods and algorithms), Image, speech and signal analysis, Multimedia and video analysis, Biometrics, Document analysis, and Bioinformatics and biomedical applications.

  • 2002 16th International Conference on Pattern Recognition


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Periodicals related to Speaker Recognition

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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


Computers, IEEE Transactions on

Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...


Consumer Electronics, IEEE Transactions on

The design and manufacture of consumer electronics products, components, and related activities, particularly those used for entertainment, leisure, and educational purposes


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Most published Xplore authors for Speaker Recognition

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Xplore Articles related to Speaker Recognition

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Robust speaker recognition based on multi-stream features

2016 IEEE International Conference on Consumer Electronics-China (ICCE-China), 2016

In this paper, we investigate the effect of the G.723.1 (6.3kbps) on speaker recognition system. In order to improve the robustness of codec mismatch, we used the Power Normalized Cepstral Coefficients (PNCC) which is a new robustness acoustic feature, to improve the performance of speaker verification system. And a modified SCF speech feature is propose to improve the robustness under ...


Multidirectional Local Feature for Speaker Recognition

2012 Third International Conference on Intelligent Systems Modelling and Simulation, 2012

This paper proposes a new feature extraction method called multi-directional local feature (MDLF) to apply on an automatic speaker recognition system. To obtain MDLF, a linear regression is applied on FFT signal in four different directions which are horizontal (time axis), vertical (frequency axis), diagonal 45 degree (time-frequency) and diagonal 135 degree (time-frequency). In the experiments, Gaussian mixture model with ...


A study on Turkish text — Dependent speaker recognition

2017 25th Signal Processing and Communications Applications Conference (SIU), 2017

Speaker recognition is a pattern recognition task which has long been studied, but the accuracies are still far from the desired levels. The majority of the studies on speaker recognition demonstrates the results obtained from databases in which English voices are used. Since there are very few studies on Turkish speech, the performance of the known successful methods in Turkish ...


Assessing the speaker recognition performance of naive listeners using mechanical turk

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011

In this paper we attempt to quantify the ability of naive listeners to perform speaker recognition in the context of the NIST evaluation task. We describe our protocol: a series of listening experiments using large numbers of naive listeners (432) on Amazon's Mechanical Turk that attempts to measure the ability of the average human listener to perform speaker recognition. Our ...


Perturbation and pitch normalization as enhancements to speaker recognition

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009

This study proposes an approach to improving speaker recognition through the process of minute vocal tract length perturbation of training files, coupled with pitch normalization for both train and test data. The notion of perturbation as a method for improving the robustness of training data for supervised classification is taken from the field of optical character recognition, where distorting characters ...


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Educational Resources on Speaker Recognition

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IEEE-USA E-Books

  • Robust speaker recognition based on multi-stream features

    In this paper, we investigate the effect of the G.723.1 (6.3kbps) on speaker recognition system. In order to improve the robustness of codec mismatch, we used the Power Normalized Cepstral Coefficients (PNCC) which is a new robustness acoustic feature, to improve the performance of speaker verification system. And a modified SCF speech feature is propose to improve the robustness under codec mismatch. We proposed a new method to improving the performance of I-vector based speaker recognition system by combining PNCC and the modified SCF feature. Three type of fusion method is introduced and compared in this paper. The experiment results of speaker recognition towards G.723.1 resynthesized coded speech demonstrate the effectiveness of our proposed method. Compared with traditional speaker recognition system, the EER improved 72% by the multi-stream features based speaker recognition system.

  • Multidirectional Local Feature for Speaker Recognition

    This paper proposes a new feature extraction method called multi-directional local feature (MDLF) to apply on an automatic speaker recognition system. To obtain MDLF, a linear regression is applied on FFT signal in four different directions which are horizontal (time axis), vertical (frequency axis), diagonal 45 degree (time-frequency) and diagonal 135 degree (time-frequency). In the experiments, Gaussian mixture model with different number of mixtures is used as classifier. Different experiments were conducted using all alphabets of Arabic for speaker recognition systems. Experimental results show that the proposed MDLF achieves better recognition accuracies than the traditional MFCC and Local features for speaker recognition system.

  • A study on Turkish text — Dependent speaker recognition

    Speaker recognition is a pattern recognition task which has long been studied, but the accuracies are still far from the desired levels. The majority of the studies on speaker recognition demonstrates the results obtained from databases in which English voices are used. Since there are very few studies on Turkish speech, the performance of the known successful methods in Turkish voices are uncertain. Therefore, in this study, the performance on the Turkish text - dependent system is investigated by using Gaussian Mixture Model - Universal Background Model (GMM - UBM) method which is a well known method in speaker recognition systems. In the experimental studies, Turkish speaker recognition database consisting of 46 speakers (36 males and 10 females) is used. Equal error rate (EER) is used to measure system performance. The equal error rate for GMM - UBM method was found to be 5.73%. It has been observed in the experiments that the speaker verification performance of GMM - UBM classifier on Turkish database is encouraging.

  • Assessing the speaker recognition performance of naive listeners using mechanical turk

    In this paper we attempt to quantify the ability of naive listeners to perform speaker recognition in the context of the NIST evaluation task. We describe our protocol: a series of listening experiments using large numbers of naive listeners (432) on Amazon's Mechanical Turk that attempts to measure the ability of the average human listener to perform speaker recognition. Our goal was to compare the performance of the average human listener to both forensic experts and state-of-the-art automatic systems. We show that naive listeners vary substantially in their performance, but that an aggregation of listener responses can achieve performance similar to that of expert forensic examiners.

  • Perturbation and pitch normalization as enhancements to speaker recognition

    This study proposes an approach to improving speaker recognition through the process of minute vocal tract length perturbation of training files, coupled with pitch normalization for both train and test data. The notion of perturbation as a method for improving the robustness of training data for supervised classification is taken from the field of optical character recognition, where distorting characters within a certain range has shown strong improvements across disparate conditions. This paper demonstrates that acoustic perturbation, in this case analysis, distortion, and resynthesis of vocal tract length for a given speaker, significantly improves speaker recognition when the resulting files are used to augment or replace the training data. A pitch length normalization technique is also discussed, which is combined with perturbation to improve open-set speaker recognition from an EER of 20% to 6.7%.

  • Feature Smoothing and Frame Reduction for Speaker Recognition

    This paper presents a new technique for smoothing and reducing speech feature vectors for speaker recognition using an adaptive weighted-sum algorithm, aims at reducing computation time and increasing the recognition performance. The proposed technique is based on a three-frame sliding window. Each step of window sliding, three feature frames in the window are used to compute weight values based on feature Euclidean distances. The weight values are applied to original MFCC feature vectors to construct smoothed feature vectors. Simultaneously, the number of smoothed vectors is reduced from the original vectors. The smoothed and reduced feature vectors are applied on an SVM speaker recognition system with GMM super vectors. The NIST Speaker Recognition Evaluation 2006 core-test is used in evaluation. Experiment results show that our approach outperforms the baseline system using conventional RASTA filtered MFCC feature vectors.

  • Improvement on the Interpersonal Relationship of the Elderly with Mild Cognitive Impairment by Using Speaker Recognition

    This study aims to develop an elderly care system for improving the interpersonal relationship of the elderly with mild cognitive impairment (MCI) by employing the speaker recognition technique. The speaker recognition units based on the Gaussian Mixture Model (GMM) and Gaussian Mixture Model-Universal Background Model (GMM-UBM) are implemented to identify the visitor via individual input utterance. Experimental results indicate that the speaker recognition unit based on GMM-UBM achieves the best performance.

  • Inter dataset variability compensation for speaker recognition

    Recently satisfactory results have been obtained in NIST speaker recognition evaluations. These results are mainly due to accurate modeling of a very large development dataset provided by LDC. However, for many realistic scenarios the use of this development dataset is limited due to a dataset mismatch. In such cases, collection of a large enough dataset is infeasible. In this work we analyze the sources of degradation for a particular setup in the context of an i-vector PLDA system and conclude that the main source for degradation is an i-vector dataset shift. As a remedy, we introduce inter dataset variability compensation (IDVC) to explicitly compensate for dataset shift in the i-vector space. This is done using the nuisance attribute projection (NAP) method. Using IDVC we managed to reduce error dramatically by more than 50% for the domain mismatch setup.

  • Duration weighted Gaussian Mixture Model supervector modeling for robust speaker recognition

    To make the supervector modeling of speech utterance more effective and accurate, this paper proposes a new duration weighted Gaussian Mixture Model (GMM) supervector modeling method for robust speaker recognition. At the beginning, this method adapts the acoustic features of speech utterance to GMM from a common basic Universal Background Model (UBM) with Maximum A Posterior (MAP) criterion and then models GMM supervector by bounding the Kullback- Leibler (KL) divergence measure. In addition, a duration weight supervector is modeled for using duration information of speech utterances. Furthermore, this paper presents a method of how to effectively apply them together during training and classification. Experimental results on American National Institute of Standards and Technology Speaker Recognition Evaluation (NIST SRE) 2008 dataset demonstrate that the proposed method outperforms the traditional GMM supervector modeling with relative 16% and 10% improvements of Equal Error Rate (EER) and Minimum Detection Cost Function (MinDCF), respectively.

  • The HKCUPU system for the NIST 2010 speaker recognition evaluation

    This paper presents the HKCUPU speaker recognition system submitted to NIST 2010 speaker recognition evaluation (SRE). The system comprises five subsystems, each with different acoustic features, session-variability reduction methods, speaker modeling and scoring methods and classifiers. This paper reports the results of individual and fusion systems for the core test and highlights the improvements made by our newly proposed JFA-Fishervoice (FSH) subsystem. Results show that FSH outperforms JFA when its projection matrix is channel-dependent (telephone or microphone) and that FSH is complementary to other state-of-the-art techniques. It was also found that VAD is an important pre-processing step for interview speech.



Standards related to Speaker Recognition

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No standards are currently tagged "Speaker Recognition"