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ICASSP 2019

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website

Robust least squares estimation of graph signals


Recovering a graph signal from samples is a central problem in graph signal processing. Least mean squares (LMS) method for graph signal estimation is computationally efficient adaptive method. In this paper, we introduce a technique to robustify LMS with respect to mismatches in the presumed graph topology. It builds on the fact that graph LMS converges faster when the graph topology is specified correctly. We consider two measures of convergence speed, based on which we develop randomized greedy algorithms for robust interpolation of graph signals.

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Authors:
Jari Miettinen, Sergiy Vorobyov, Esa Ollila
Submitted On:
13 May 2019 - 12:55pm
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[1] Jari Miettinen, Sergiy Vorobyov, Esa Ollila, "Robust least squares estimation of graph signals", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4489. Accessed: Jul. 19, 2019.
@article{4489-19,
url = {http://sigport.org/4489},
author = {Jari Miettinen; Sergiy Vorobyov; Esa Ollila },
publisher = {IEEE SigPort},
title = {Robust least squares estimation of graph signals},
year = {2019} }
TY - EJOUR
T1 - Robust least squares estimation of graph signals
AU - Jari Miettinen; Sergiy Vorobyov; Esa Ollila
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4489
ER -
Jari Miettinen, Sergiy Vorobyov, Esa Ollila. (2019). Robust least squares estimation of graph signals. IEEE SigPort. http://sigport.org/4489
Jari Miettinen, Sergiy Vorobyov, Esa Ollila, 2019. Robust least squares estimation of graph signals. Available at: http://sigport.org/4489.
Jari Miettinen, Sergiy Vorobyov, Esa Ollila. (2019). "Robust least squares estimation of graph signals." Web.
1. Jari Miettinen, Sergiy Vorobyov, Esa Ollila. Robust least squares estimation of graph signals [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4489

In-Car Driver Authentication Using Wireless Sensing


Automobiles have become an essential part of everyday lives. In this work, we attempt to make them smarter by introducing the idea of in-car driver authentication using wireless sensing. Our aim is to develop a model which can recognize drivers automatically. Firstly, we address the problem of "changing in-car environments", where the existing wireless sensing based human identification system fails. To this end, we build the first in-car driver radio biometric dataset to understand the effect of changing environments on human radio biometrics.

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Authors:
Beibei Wang
Submitted On:
13 May 2019 - 11:17am
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[1] Beibei Wang, "In-Car Driver Authentication Using Wireless Sensing", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4487. Accessed: Jul. 19, 2019.
@article{4487-19,
url = {http://sigport.org/4487},
author = {Beibei Wang },
publisher = {IEEE SigPort},
title = {In-Car Driver Authentication Using Wireless Sensing},
year = {2019} }
TY - EJOUR
T1 - In-Car Driver Authentication Using Wireless Sensing
AU - Beibei Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4487
ER -
Beibei Wang. (2019). In-Car Driver Authentication Using Wireless Sensing. IEEE SigPort. http://sigport.org/4487
Beibei Wang, 2019. In-Car Driver Authentication Using Wireless Sensing. Available at: http://sigport.org/4487.
Beibei Wang. (2019). "In-Car Driver Authentication Using Wireless Sensing." Web.
1. Beibei Wang. In-Car Driver Authentication Using Wireless Sensing [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4487

AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION


This paper proposes a group membership verification protocol preventing the curious but honest server from reconstructing the enrolled signatures and inferring the identity of querying clients. The protocol quantizes the signatures into discrete embeddings, making reconstruction difficult. It also aggregates multiple embeddings into representative values, impeding identification. Theoretical and experimental results show the trade-off between the security and error rates.

Paper Details

Authors:
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy
Submitted On:
13 May 2019 - 9:23am
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[1] Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy, "AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4486. Accessed: Jul. 19, 2019.
@article{4486-19,
url = {http://sigport.org/4486},
author = {Marzieh Gheisari; Teddy Furon; Laurent Amsaleg; Behrooz Razeghi; Slava Voloshynovskiy },
publisher = {IEEE SigPort},
title = {AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION},
year = {2019} }
TY - EJOUR
T1 - AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION
AU - Marzieh Gheisari; Teddy Furon; Laurent Amsaleg; Behrooz Razeghi; Slava Voloshynovskiy
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4486
ER -
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. (2019). AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION. IEEE SigPort. http://sigport.org/4486
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy, 2019. AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION. Available at: http://sigport.org/4486.
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. (2019). "AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION." Web.
1. Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4486

Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks

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Authors:
Sumit Jha, Carlos Busso
Submitted On:
13 May 2019 - 9:22am
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[1] Sumit Jha, Carlos Busso, "Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4485. Accessed: Jul. 19, 2019.
@article{4485-19,
url = {http://sigport.org/4485},
author = {Sumit Jha; Carlos Busso },
publisher = {IEEE SigPort},
title = {Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks},
year = {2019} }
TY - EJOUR
T1 - Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks
AU - Sumit Jha; Carlos Busso
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4485
ER -
Sumit Jha, Carlos Busso. (2019). Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks. IEEE SigPort. http://sigport.org/4485
Sumit Jha, Carlos Busso, 2019. Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks. Available at: http://sigport.org/4485.
Sumit Jha, Carlos Busso. (2019). "Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks." Web.
1. Sumit Jha, Carlos Busso. Estimation of Gaze Region using Two Dimensional Probabilistic Maps Constructed using Convolutional Neural Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4485

AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION


This paper proposes a group membership verification protocol preventing the curious but honest server from reconstructing the enrolled signatures and inferring the identity of querying clients. The protocol quantizes the signatures into discrete embeddings, making reconstruction difficult. It also aggregates multiple embeddings into representative values, impeding identification. Theoretical and experimental results show the trade-off between the security and error rates.

Paper Details

Authors:
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy
Submitted On:
13 May 2019 - 9:23am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

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conference_poster_4.pdf

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[1] Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy, "AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4484. Accessed: Jul. 19, 2019.
@article{4484-19,
url = {http://sigport.org/4484},
author = {Marzieh Gheisari; Teddy Furon; Laurent Amsaleg; Behrooz Razeghi; Slava Voloshynovskiy },
publisher = {IEEE SigPort},
title = {AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION},
year = {2019} }
TY - EJOUR
T1 - AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION
AU - Marzieh Gheisari; Teddy Furon; Laurent Amsaleg; Behrooz Razeghi; Slava Voloshynovskiy
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4484
ER -
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. (2019). AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION. IEEE SigPort. http://sigport.org/4484
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy, 2019. AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION. Available at: http://sigport.org/4484.
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. (2019). "AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION." Web.
1. Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4484

wav2letter++ : A Fast Open-Source Speech Recognition Framework


This paper introduces wav2letter++, a fast open-source deep learning speech recognition framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor library for maximum efficiency. Here we explain the architecture and design of the wav2letter++ system and compare it to other major open-source speech recognition systems. In some cases wav2letter++ is more than 2x faster than other optimized frameworks for training end-to-end neural networks for speech recognition.

Paper Details

Authors:
Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert
Submitted On:
13 May 2019 - 8:40am
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[1] Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert, "wav2letter++ : A Fast Open-Source Speech Recognition Framework", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4483. Accessed: Jul. 19, 2019.
@article{4483-19,
url = {http://sigport.org/4483},
author = {Vineel Pratap; Awni Hannun; Qiantong Xu; Jeff Cai; Jacob Kahn; Gabriel Synnaeve; Vitaliy Liptchinsky; Ronan Collobert },
publisher = {IEEE SigPort},
title = {wav2letter++ : A Fast Open-Source Speech Recognition Framework},
year = {2019} }
TY - EJOUR
T1 - wav2letter++ : A Fast Open-Source Speech Recognition Framework
AU - Vineel Pratap; Awni Hannun; Qiantong Xu; Jeff Cai; Jacob Kahn; Gabriel Synnaeve; Vitaliy Liptchinsky; Ronan Collobert
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4483
ER -
Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert. (2019). wav2letter++ : A Fast Open-Source Speech Recognition Framework. IEEE SigPort. http://sigport.org/4483
Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert, 2019. wav2letter++ : A Fast Open-Source Speech Recognition Framework. Available at: http://sigport.org/4483.
Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert. (2019). "wav2letter++ : A Fast Open-Source Speech Recognition Framework." Web.
1. Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert. wav2letter++ : A Fast Open-Source Speech Recognition Framework [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4483

A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks


Lung cancer is the most prevalent cancer worldwide with about 230,000 new cases every year. Most cases go undiagnosed until it’s too late, especially in developing countries and remote areas. Early detection is key to beating cancer. Towards this end, the work presented here proposes an automated pipeline for lung tumor detection and segmentation from 3D lung CT scans from the NSCLC Radiomics Dataset. It also presents a new dilated hybrid-3D convolutional neural network architecture for tumor segmentation. First, a binary classifier chooses CT scan slices that may contain parts of a tumor.

Paper Details

Authors:
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque
Submitted On:
16 May 2019 - 8:05am
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LungNet3D-Poster

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[1] Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque, "A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4482. Accessed: Jul. 19, 2019.
@article{4482-19,
url = {http://sigport.org/4482},
author = {Shahruk Hossain; Suhail Najeeb; Asif Shahriyar; Zaowad Rahabin Abdullah; Mohammad Ariful Haque },
publisher = {IEEE SigPort},
title = {A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks},
year = {2019} }
TY - EJOUR
T1 - A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks
AU - Shahruk Hossain; Suhail Najeeb; Asif Shahriyar; Zaowad Rahabin Abdullah; Mohammad Ariful Haque
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4482
ER -
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque. (2019). A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks. IEEE SigPort. http://sigport.org/4482
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque, 2019. A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks. Available at: http://sigport.org/4482.
Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque. (2019). "A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks." Web.
1. Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad Rahabin Abdullah, Mohammad Ariful Haque. A Pipeline for Lung Tumor Detection and Segmentation from CT Scans using Dilated Convolutional Neural Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4482

HIERARCHY-AWARE LOSS FUNCTION ON A TREE STRUCTURED LABEL SPACE FOR AUDIO EVENT DETECTION

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Authors:
Arindam Jati, Naveen Kumar, Ruxin Chen, Panayiotis Georgiou
Submitted On:
14 May 2019 - 7:11am
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[1] Arindam Jati, Naveen Kumar, Ruxin Chen, Panayiotis Georgiou, "HIERARCHY-AWARE LOSS FUNCTION ON A TREE STRUCTURED LABEL SPACE FOR AUDIO EVENT DETECTION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4481. Accessed: Jul. 19, 2019.
@article{4481-19,
url = {http://sigport.org/4481},
author = {Arindam Jati; Naveen Kumar; Ruxin Chen; Panayiotis Georgiou },
publisher = {IEEE SigPort},
title = {HIERARCHY-AWARE LOSS FUNCTION ON A TREE STRUCTURED LABEL SPACE FOR AUDIO EVENT DETECTION},
year = {2019} }
TY - EJOUR
T1 - HIERARCHY-AWARE LOSS FUNCTION ON A TREE STRUCTURED LABEL SPACE FOR AUDIO EVENT DETECTION
AU - Arindam Jati; Naveen Kumar; Ruxin Chen; Panayiotis Georgiou
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4481
ER -
Arindam Jati, Naveen Kumar, Ruxin Chen, Panayiotis Georgiou. (2019). HIERARCHY-AWARE LOSS FUNCTION ON A TREE STRUCTURED LABEL SPACE FOR AUDIO EVENT DETECTION. IEEE SigPort. http://sigport.org/4481
Arindam Jati, Naveen Kumar, Ruxin Chen, Panayiotis Georgiou, 2019. HIERARCHY-AWARE LOSS FUNCTION ON A TREE STRUCTURED LABEL SPACE FOR AUDIO EVENT DETECTION. Available at: http://sigport.org/4481.
Arindam Jati, Naveen Kumar, Ruxin Chen, Panayiotis Georgiou. (2019). "HIERARCHY-AWARE LOSS FUNCTION ON A TREE STRUCTURED LABEL SPACE FOR AUDIO EVENT DETECTION." Web.
1. Arindam Jati, Naveen Kumar, Ruxin Chen, Panayiotis Georgiou. HIERARCHY-AWARE LOSS FUNCTION ON A TREE STRUCTURED LABEL SPACE FOR AUDIO EVENT DETECTION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4481

The discrete cosine transform on triangles

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Authors:
Knut Hüper
Submitted On:
13 May 2019 - 8:12am
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[1] Knut Hüper, "The discrete cosine transform on triangles", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4480. Accessed: Jul. 19, 2019.
@article{4480-19,
url = {http://sigport.org/4480},
author = {Knut Hüper },
publisher = {IEEE SigPort},
title = {The discrete cosine transform on triangles},
year = {2019} }
TY - EJOUR
T1 - The discrete cosine transform on triangles
AU - Knut Hüper
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4480
ER -
Knut Hüper. (2019). The discrete cosine transform on triangles. IEEE SigPort. http://sigport.org/4480
Knut Hüper, 2019. The discrete cosine transform on triangles. Available at: http://sigport.org/4480.
Knut Hüper. (2019). "The discrete cosine transform on triangles." Web.
1. Knut Hüper. The discrete cosine transform on triangles [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4480

DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION


Speech signal contains intrinsic and extrinsic variations such as accent, emotion, dialect, phoneme, speaking manner, noise, music, and reverberation. Some of these variations are unnecessary and are unspecified factors of variation. These factors lead to increased variability in speaker representation. In this paper, we assume that unspecified factors of variation exist in speaker representations, and we attempt to minimize variability in speaker representation.

Paper Details

Authors:
Insoo Kim, Kyuhong Kim, Jiwhan Kim, Changkyu Choi
Submitted On:
13 May 2019 - 2:29am
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[1] Insoo Kim, Kyuhong Kim, Jiwhan Kim, Changkyu Choi, "DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4477. Accessed: Jul. 19, 2019.
@article{4477-19,
url = {http://sigport.org/4477},
author = {Insoo Kim; Kyuhong Kim; Jiwhan Kim; Changkyu Choi },
publisher = {IEEE SigPort},
title = {DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION},
year = {2019} }
TY - EJOUR
T1 - DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION
AU - Insoo Kim; Kyuhong Kim; Jiwhan Kim; Changkyu Choi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4477
ER -
Insoo Kim, Kyuhong Kim, Jiwhan Kim, Changkyu Choi. (2019). DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION. IEEE SigPort. http://sigport.org/4477
Insoo Kim, Kyuhong Kim, Jiwhan Kim, Changkyu Choi, 2019. DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION. Available at: http://sigport.org/4477.
Insoo Kim, Kyuhong Kim, Jiwhan Kim, Changkyu Choi. (2019). "DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION." Web.
1. Insoo Kim, Kyuhong Kim, Jiwhan Kim, Changkyu Choi. DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4477

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