Searching for Robust Asr Using Support Vector Machines information? Find all needed info by using official links provided below.
https://www.sciencedirect.com/science/article/pii/S0167639307000246
In (Smith and Gales, 2002) some generalisations of this kernel are evaluated in the ASR framework. 3. Support vector machine fundamentals. In this section, our purpose is to introduce the basic notions of Support Vector Machines emphasising the characteristics related with their use in speech recognition.Cited by: 67
https://www.researchgate.net/publication/33399237_Robust_ASR_using_Support_Vector_Machines
The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a …
https://www.sciencedirect.com/science/article/abs/pii/S0167639307000246
The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition.Cited by: 67
https://core.ac.uk/download/pdf/29427988.pdf
Robust ASR using Support Vector Machines R. Solera-Uren˜a, D. Mart´ın-Iglesias, A. Gallardo-Antol´ın, C. Pel´aez-Moreno∗, F. D´ıaz-de-Mar´ıa Signal Theory and Communications Department, EPS-Universidad Carlos III de Madrid, Legan´es-28911, Spain Abstract The improved theoretical properties of Support Vector Machines with respect to
https://www.researchgate.net/publication/224096796_Support_Vector_Machines_for_Noise_Robust_ASR
Using discriminative classifiers, such as Support Vector Machines (SVMs) in combination with, or as an alternative to, Hidden Markov Models (HMMs) has a …
http://www.cs.unc.edu/~jeffay/courses/nidsS05/ai/robust-anomaly-detection-using.pdf
Abstract—Using the 1998 DARPA BSM data set collected at MIT’s Lincoln Labs to study intrusion detection systems, the performance of robust support vector machines (RSVMs) was compared with that of conventional support vector machines and nearest neighbor classifiers in separating normal usage profiles from intrusive profiles of computer ...
http://mi.eng.cam.ac.uk/~mjfg/gales_ASRU09.pdf
Support Vector Machines for Noise Robust ASR M. J. F. Gales, A. Ragni, H. AlDamarki and C. Gautier Cambridge University Engineering Department Trumpington St., Cambridge CB2 1PZ, U.K. {mjfg,ar527,hia21,cg291}@eng.cam.ac.uk Abstract—Using discriminative classifiers, such as Support Vector Machines (SVMs) in combination with, or as an alter-
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.422.8996&rep=rep1&type=pdf
Support vector machines are receiving increasing attention as a tool for speech recognition applications [7, 24–26, 32, 39– 41]. The main aim of the present work is to find represen-tations along with corresponding kernels and effective noise compensation methods for noise robust speech recognition using SVMs.
https://core.ac.uk/display/29427988
Abstract. The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition.Cited by: 67
https://arxiv.org/pdf/1401.3322v1
A Subband-Based SVM Front-End for Robust ASR ... SUBBAND CLASSIFICATION USING SUPPORT VECTOR MACHINES Support vector machines (SVMs) are receiving increasing attention as a tool for speech recognition applications due to their good generalization properties [17, 35, 42, 43, 45–48]. Here we use them inAuthor: Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich, Matthew Ager
https://www.sciencedirect.com/science/article/pii/S0167639307000246
In (Smith and Gales, 2002) some generalisations of this kernel are evaluated in the ASR framework. 3. Support vector machine fundamentals. In this section, our purpose is to introduce the basic notions of Support Vector Machines emphasising the characteristics related with their use in speech recognition.Cited by: 67
https://www.researchgate.net/publication/33399237_Robust_ASR_using_Support_Vector_Machines
The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good...
https://core.ac.uk/download/pdf/29427988.pdf
The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition.
http://mi.eng.cam.ac.uk/~mjfg/gales_ASRU09.pdf
Support Vector Machines for Noise Robust ASR M. J. F. Gales, A. Ragni, H. AlDamarki and C. Gautier Cambridge University Engineering Department Trumpington St., Cambridge CB2 1PZ, U.K. {mjfg,ar527,hia21,cg291}@eng.cam.ac.uk Abstract—Using discriminative classifiers, such as Support Vector Machines (SVMs) in combination with, or as an alter-
https://ui.adsabs.harvard.edu/abs/2014arXiv1401.3322Y/abstract
This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-end that operates on an ensemble of the subband components of high-dimensional acoustic waveforms.Author: Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich, Matthew Ager
https://arxiv.org/pdf/1401.3322v1
Abstract— This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-endthat operates on an ensemble of the subband components of high-dimensional acousticAuthor: Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich, Matthew Ager
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000006097045
Hidden Markov models Support vector machines Training Speech recognition Speech Artificial neural networks Real time systems SVM/HMM Additive noise artificial neural network (ANN)/hidden Markov model (HMM) compact support vector machine (SVM) hybrid automatic speech recognition (ASR) machine learning real-time ASR robust ASRCited by: 19
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.7857
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract—Using discriminative classifiers, such as Support Vector Machines (SVMs) in combination with, or as an alternative to, Hidden Markov Models (HMMs) has a number of advantages for difficult speech recognition tasks. For example, the models can make use of additional dependencies in the …
http://www.cs.unc.edu/~jeffay/courses/nidsS05/ai/robust-anomaly-detection-using.pdf
Abstract—Using the 1998 DARPA BSM data set collected at MIT’s Lincoln Labs to study intrusion detection systems, the performance of robust support vector machines (RSVMs) was compared with that of conventional support vector machines and nearest neighbor classifiers in separating normal usage profiles from intrusive profiles of computer programs.
https://www.sciencedirect.com/science/article/pii/S0167639307000246
In (Smith and Gales, 2002) some generalisations of this kernel are evaluated in the ASR framework. 3. Support vector machine fundamentals. In this section, our purpose is to introduce the basic notions of Support Vector Machines emphasising the characteristics related with their use in speech recognition.Cited by: 66
https://www.researchgate.net/publication/33399237_Robust_ASR_using_Support_Vector_Machines
The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good ...
https://www.sciencedirect.com/science/article/abs/pii/S0167639307000246
The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition.Cited by: 66
https://core.ac.uk/download/pdf/29427988.pdf
Robust ASR using Support Vector Machines R. Solera-Uren˜a, D. Mart´ın-Iglesias, A. Gallardo-Antol´ın, C. Pel´aez-Moreno∗, F. D´ıaz-de-Mar´ıa Signal Theory and Communications Department, EPS-Universidad Carlos III de Madrid, Legan´es-28911, Spain Abstract The improved theoretical properties of Support Vector Machines with respect to
https://www.researchgate.net/publication/224096796_Support_Vector_Machines_for_Noise_Robust_ASR
Using discriminative classifiers, such as Support Vector Machines (SVMs) in combination with, or as an alternative to, Hidden Markov Models (HMMs) has a number of advantages for difficult speech ...
http://www.cs.unc.edu/~jeffay/courses/nidsS05/ai/robust-anomaly-detection-using.pdf
Abstract—Using the 1998 DARPA BSM data set collected at MIT’s Lincoln Labs to study intrusion detection systems, the performance of robust support vector machines (RSVMs) was compared with that of conventional support vector machines and nearest neighbor classifiers in separating normal usage profiles from intrusive profiles of computer ...
https://core.ac.uk/display/29427988
Abstract. The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition.Cited by: 66
http://mi.eng.cam.ac.uk/~mjfg/gales_ASRU09.pdf
Support Vector Machines for Noise Robust ASR M. J. F. Gales, A. Ragni, H. AlDamarki and C. Gautier Cambridge University Engineering Department Trumpington St., Cambridge CB2 1PZ, U.K. {mjfg,ar527,hia21,cg291}@eng.cam.ac.uk Abstract—Using discriminative classifiers, such as Support Vector Machines (SVMs) in combination with, or as an alter-
https://arxiv.org/pdf/1401.3322v1
A Subband-Based SVM Front-End for Robust ASR ... SUBBAND CLASSIFICATION USING SUPPORT VECTOR MACHINES Support vector machines (SVMs) are receiving increasing attention as a tool for speech recognition applications due to their good generalization …Author: Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich, Matthew Ager
https://ui.adsabs.harvard.edu/abs/2014arXiv1401.3322Y/abstract
This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-end that operates on an ensemble of the subband components of high-dimensional acoustic waveforms. The key issues of selecting the appropriate SVM kernels for classification in frequency subbands and the combination of individual subband classifiers using …
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6097045
Real-Time Robust Automatic Speech Recognition Using Compact Support Vector Machines Abstract: In the last years, support vector machines (SVMs) have shown excellent performance in many applications, especially in the presence of noise.
https://nms.kcl.ac.uk/peter.sollich/papers_pdf/Yousafzai_journal10.pdf
A High-Dimensional Subband Speech Representation and SVM Framework for Robust Speech Recognition Jibran Yousafzai∗†, Member, IEEE Zoran Cvetkovic´†, Senior Member, IEEE Peter Sollich‡ Matthew Ager‡ Abstract— This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000006097045
Hidden Markov models Support vector machines Training Speech recognition Speech Artificial neural networks Real time systems SVM/HMM Additive noise artificial neural network (ANN)/hidden Markov model (HMM) compact support vector machine (SVM) hybrid automatic speech recognition (ASR) machine learning real-time ASR robust ASR
https://link.springer.com/chapter/10.1007/978-3-319-94649-8_36
In this paper, a novel deep neural networks (DN) using Support Vector Machines (SVM) instead of the multinomial logistic regression is proposed. We have verified the effectiveness of this new method using speech samples from Aurora speech database recorded in noisy conditions.
https://arxiv.org/abs/1401.3322
This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-end that operates on an ensemble of the subband components of high-dimensional acoustic waveforms. The key issues of selecting the appropriate SVM kernels for classification in frequency subbands and the combination of individual subband classifiers using …
https://www.ripublication.com/ijaer18/ijaerv13n10_170.pdf
algorithm for noise robust ASR system is represented as follows: The training for the ISVM-PLDA (Inverse Support Vector Machine) algorithm differs from SVM-PLDA only in the organization of the learning data in the opposite direction. The organization of learning data in ISVM-PLDA may seem
https://www.icsi.berkeley.edu/pubs/speech/hybridmlp14.pdf
Hybrid MLP/Structured-SVM Tandem Systems for Large Vocabulary and Robust ASR Suman V. Ravuri1,2 1International Computer Science Institute, Berkeley, CA, USA 2EECS Department, University of California - Berkeley, Berkeley, CA, USA [email protected] Abstract Tandem systems based on multi-layer perceptrons (MLPs)
http://mi.eng.cam.ac.uk/~mjfg/gales_INTER08.pdf
Discriminative Classifiers with Generative Kernels for Noise Robust ASR M.J.F. Gales and C. Longworth Cambridge University Engineering Department Trumpington St., Cambridge CB2 1PZ, U.K. ... tive models, or discriminative functions such as Support Vector Machines (SVMs) [1]. One of the problems with using these
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.8065
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract — This work explores the potential for robust classification of phonemes in the presence of additive noise and linear filtering using high-dimensional features in the subbands of acoustic waveforms. The proposed technique is compared with state-of-the-art automatic speech recognition (ASR) front-ends on the ...
https://pdfs.semanticscholar.org/204d/96c5fd36485a6cbdc96aeb20f2c52b9fc701.pdf
, we have developed a robust Support Vector Machines (SVM) scheme of classifying imbalanced and noisy data using the principles of Robust Optimization. Uncertainty is prevalent in almost all datasets and has not been addressed efficiently by most data mining techniques, as these are based on deterministic mathematical tools.
https://ieeexplore.ieee.org/document/1439244/
Robust array beamforming with sidelobe control using support vector machines Abstract: Robust adaptive beamforming is a challenging task in wireless communications due to the strict restrictions in the number of available snapshots, signal mismatches, or calibration errors. We present a new approach to adaptive beamforming that provides ...
http://www.seas.ucla.edu/spapl/paper/you_interspeech_09.pdf
Temporal Modulation Processing of Speech Signals for Noise Robust ASR Hong You, Abeer Alwan Department of Electrical Engineering, University of California, Los Angeles, CA 90095 ... feature classification task using linear support vector machines (SVMs) trained to classify speech/noise modulation features.
http://web.cse.ohio-state.edu/~wang.77/papers/Wang-Wang.taslp16.pdf
A Joint Training Framework for Robust Automatic Speech Recognition ... WANG AND WANG: JOINT TRAINING FRAMEWORK FOR ROBUST AUTOMATIC SPEECH RECOGNITION 797 ... learning machines, such as GMMs, support vector machines (SVMs) and multi-layer perceptrons (MLPs), have been used
How to find Robust Asr Using Support Vector Machines information?
Follow the instuctions below:
- Choose an official link provided above.
- Click on it.
- Find company email address & contact them via email
- Find company phone & make a call.
- Find company address & visit their office.
