4,284 resources related to Linear regression
- Topics related to Linear regression
- IEEE Organizations related to Linear regression
- Conferences related to Linear regression
- Periodicals related to Linear regression
- Most published Xplore authors for Linear regression
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore
International Geosicence and Remote Sensing Symposium (IGARSS) is the annual conference sponsored by the IEEE Geoscience and Remote Sensing Society (IEEE GRSS), which is also the flagship event of the society. The topics of IGARSS cover a wide variety of the research on the theory, techniques, and applications of remote sensing in geoscience, which includes: the fundamentals of the interactions electromagnetic waves with environment and target to be observed; the techniques and implementation of remote sensing for imaging and sounding; the analysis, processing and information technology of remote sensing data; the applications of remote sensing in different aspects of earth science; the missions and projects of earth observation satellites and airborne and ground based campaigns. The theme of IGARSS 2019 is “Enviroment and Disasters”, and some emphases will be given on related special topics.
The conference covers all aspects of the technology associated with ultrasound generation and detection and their applications.
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.
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; ...
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...
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.
Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...
IEEE Transactions on Audio, Speech, and Language Processing, 2010
Incremental adaptation techniques for speech recognition are aimed at adjusting acoustic models to time-variant acoustic characteristics related to such factors as changes of speaker, speaking style, and noise source over time. In this paper, we propose a novel incremental adaptation framework, which models such time-variant characteristics by successively updating posterior distributions of acoustic model parameters based on a macroscopic time ...
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2001
This paper presents an online/sequential linear regression adaptation framework for hidden Markov model (HMM) based speech recognition. Our attempt is to sequentially improve the speaker-independent (SI) speech recognizer to meet nonstationary environments via linear regression adaptation of SI HMMs. A quasi-Bayes linear regression (QBLR) algorithm is developed to execute online adaptation where the regression matrix is estimated using QB theory. ...
IEEE Transactions on Audio, Speech, and Language Processing, 2007
In this paper, we propose an application of kernel methods for fast speaker adaptation based on kernelizing the eigenspace-based maximum-likelihood linear regression adaptation method. We call our new method "kernel eigenspace-based maximum-likelihood linear regression adaptation" (KEMLLR). In KEMLLR, speaker- dependent (SD) models are estimated from a common speaker-independent (SI) model using MLLR adaptation, and the MLLR transformation matrices are mapped ...
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
This paper presents an investigation of the minimum verification error linear regression (MVELR) method for discriminative linear-transform based adaptation. The MVE criterion is employed to estimate a set of discriminative linear transformations which achieve the smallest empirical average loss with the given adaptation data. The MVELR directly minimizes the total detection errors, some of which are results of characteristic mismatch ...
2005 IEEE International Conference on Multimedia and Expo, 2005
Multi-stream hidden Markov models (HMMs) have recently been very successful in audio-visual speech recognition, where the audio and visual streams are fused at the final decision level. In this paper we investigate fast feature space speaker adaptation using multi-stream HMMs for audio-visual speech recognition. In particular, we focus on studying the performance of feature- space maximum likelihood linear regression (fMLLR), ...
Single Frame Super Resolution: Fuzzy Rule-Based and Gaussian Mixture Regression Approaches
IMS 2012 Microapps - RF System Design: Moving Beyond a Linear Datasheet
IMS 2011 Microapps - STAN Tool: A New Method for Linear and Nonlinear Stability Analysis of Microwave Circuits
A 20dBm Configurable Linear CMOS RF Power Amplifier for Multi-Standard Transmitters: RFIC Industry Showcase
A High-Efficiency Linear Power Amplifier for 28GHz Mobile Communications in 40nm CMOS: RFIC Interactive Forum 2017
Co-design of Power Amplifier and Dynamic Power Supplies for Radar and Communications Transmitters
Advances in Kernel Methods
Phase Retrieval with Application to Optical Imaging
Provably-Correct Robot Control with LTLMoP, OMPL and ROS
Parallelized Linear Classification with Volumetric Chemical Perceptrons - Jacob Rosenstein - ICRC 2018
Sparse Fuzzy Modeling - Nikhil R Pal - WCCI 2016
Learning through Deterministic Assignment of Hidden Parameter
WIE ILC 2015 - Being Fearless: A Keynote with Patty Hatter of Intel
Superconducting RF Cavities and Future Particle Accelerators - Applied Superconductivity Conference 2018
Tutorial: Model Predictive Control of Power Electronic Converters, Part Two, Tomislav Dragicevic - IECON 2018
Micro-Apps 2013: Design Methodology for GaAs MMIC PA
Interaction of ferromagnetic and superconducting permanent magnets - superconducting levitation
Tutorial: Model Predictive Control of Power Electronic Converters, Part One, Jose Rodriguez - IECON 2018
A 200um x 200um x 100um, 63nW, 2.4GHz Injectable Fully-Monolithic Wireless BioSensing System: RFIC Industry Showcase 2017
Incremental adaptation techniques for speech recognition are aimed at adjusting acoustic models to time-variant acoustic characteristics related to such factors as changes of speaker, speaking style, and noise source over time. In this paper, we propose a novel incremental adaptation framework, which models such time-variant characteristics by successively updating posterior distributions of acoustic model parameters based on a macroscopic time scale (e.g., every set of more than a dozen utterances). The proposed incremental update involves a predictor-corrector algorithm based on a macroscopic time evolution system in accordance with the Kalman filter theory. We also provide a unified interpretation of the proposal and the two major conventional approaches of indirect adaptation via transformation parameters [e.g., maximum-likelihood linear regression (MLLR)] and direct adaptation of classifier parameters [e.g., maximum_a posteriori_(MAP)]. We reveal analytically and experimentally that the proposed incremental adaptation realizes the predictor-corrector algorithm and involves both the conventional and their combinatorial adaptation approaches. Consequently, the proposal achieves robust recognition performance based on a balanced incremental adaptation between quickness and stability.
This paper presents an online/sequential linear regression adaptation framework for hidden Markov model (HMM) based speech recognition. Our attempt is to sequentially improve the speaker-independent (SI) speech recognizer to meet nonstationary environments via linear regression adaptation of SI HMMs. A quasi-Bayes linear regression (QBLR) algorithm is developed to execute online adaptation where the regression matrix is estimated using QB theory. In the estimation, we moderately specify the prior density of the regression matrix as a matrix variate normal distribution and exactly derive the pooled posterior density belonging to the same distribution family. Accordingly, the optimal regression matrix can be easily calculated. Also, the reproducible prior/posterior density pair provides a meaningful mechanism for sequential learning of prior statistics. At each sequential epoch, only the updated prior statistics and the current observed data are required for adaptation. In general, the proposed QBLR is universal and can be reduced to the well-known maximum likelihood linear regression (MLLR) and maximum a posteriori linear regression (MAPLR). Experiments show that the QBLR is effective for speaker adaptation in car environments.
In this paper, we propose an application of kernel methods for fast speaker adaptation based on kernelizing the eigenspace-based maximum-likelihood linear regression adaptation method. We call our new method "kernel eigenspace-based maximum-likelihood linear regression adaptation" (KEMLLR). In KEMLLR, speaker- dependent (SD) models are estimated from a common speaker-independent (SI) model using MLLR adaptation, and the MLLR transformation matrices are mapped to a kernel-induced high-dimensional feature space, wherein kernel principal component analysis is used to derive a set of eigenmatrices. In addition, a composite kernel is used to preserve row information in the transformation matrices. A new speaker's MLLR transformation matrix is then represented as a linear combination of the leading kernel eigenmatrices, which, though exists only in the feature space, still allows the speaker's mean vectors to be found explicitly. As a result, at the end of KEMLLR adaptation, a regular hidden Markov model (HMM) is obtained for the new speaker and subsequent speech recognition is as fast as normal HMM decoding. KEMLLR adaptation was tested and compared with other adaptation methods on the Resource Management and Wall Street Journal tasks using 5 or 10 s of adaptation speech. In both cases, KEMLLR adaptation gives the greatest improvement over the SI model with 11%-20% word error rate reduction
This paper presents an investigation of the minimum verification error linear regression (MVELR) method for discriminative linear-transform based adaptation. The MVE criterion is employed to estimate a set of discriminative linear transformations which achieve the smallest empirical average loss with the given adaptation data. The MVELR directly minimizes the total detection errors, some of which are results of characteristic mismatch in the given adaptation data. In this study, segment-based phonetic detectors reflecting an important processing layer in speech event detection are initially trained via the conventional maximum likelihood (ML) method and then refined via the general MVE method using the original training data. Then, the initial MVE- trained detectors are adapted by two kinds of adaption techniques, MLLR and MVELR, respectively, with the given adaptation data for comparison. The experiments are performed on a supervised adaptation scenario and the effectiveness of the adapted detectors is evaluated based on the total detection error. Experimental results confirm the proposed MVELR method considerably reduces the total error rate over all categories of the detectors compared to the MLLR.
Multi-stream hidden Markov models (HMMs) have recently been very successful in audio-visual speech recognition, where the audio and visual streams are fused at the final decision level. In this paper we investigate fast feature space speaker adaptation using multi-stream HMMs for audio-visual speech recognition. In particular, we focus on studying the performance of feature- space maximum likelihood linear regression (fMLLR), a fast and effective method for estimating feature space transforms. Unlike the common speaker adaptation techniques of MAP or MLLR, fMLLR does not change the audio or visual HMM parameters, but simply applies a single transform to the testing features. We also address the problem of fast and robust on-line fMLLR adaptation using feature space maximum a posterior linear regression (fMAPLR). Adaptation experiments are reported on the IBM infrared headset audio-visual database. On average for a 20-speaker 1 hour independent test set, the multi- stream fMLLR achieves 31% relative gain on the clean audio condition, and 59% relative gain on the noisy audio condition (approximately 7 dB) as compared to the baseline multi-stream system
The discounted likelihood procedure, which is a robust extension of the usual EM procedure, is presented, and two approximations which lead to two different variants of the usual maximum likelihood linear regression adaptation scheme are introduced. These schemes are shown to robustly estimate speaker adaptation transforms with very little data. The evaluation is carried out on the Switchboard corpus.
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.
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.
Maximum likelihood linear regression (MLLR) is a parameter transformation technique for both speaker and environment adaptation. In this paper, the iterative use of MLLR is investigated in the context of large-vocabulary speaker-independent transcription of both noise-free and noisy data. It is shown that iterative application of MLLR can be beneficial especially in situations of severe mismatch. When word lattices are used, it is important that the lattices contain the correct transcription, and it is shown that global MLLR based on rough initial transcriptions of the data can be very useful in generating high-quality lattices. MLLR can also be used in an iterative fashion to refine the transcriptions of the test data and to adapt models based on the current transcriptions. These techniques were used by the HTK large-vocabulary speech recognition system for the November 1995 ARPA H3 evaluation. It is shown that iterative-application MLLR proved to be very effective prior to lattice generation and for iterative refinement.
This paper describes an online handwriting recognition system with focus on adaptation techniques. Our hidden Markov model (HMM)-based recognition system for cursive German script can be adapted to the writing style of a new writer using either a retraining depending on the EM (expectation maximization)-approach or an adaptation according to the MAP (maximum a posteriori) or MLLR (maximum likelihood linear regression)-criterion. The performance of the resulting writer-dependent system increases significantly even if the amount of adaptation data is very small (about 6 words). So this approach is also applicable for online systems in hand-held computers such as PDAs. Special attention was paid to the performance comparison of the different adaptation techniques with the availability of different amounts of adaptation data ranging from a few words tip to 100 words per writer.
No standards are currently tagged "Linear regression"