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Neural network learning (MLR-NNLR)

DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS


Speech-related Brain Computer Interfaces (BCI) aim primarily at finding an alternative vocal communication pathway for
people with speaking disabilities. As a step towards full decoding of imagined speech from active thoughts, we present a
BCI system for subject-independent classification of phonological categories exploiting a novel deep learning based

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Authors:
Pramit Saha, Muhammad Abdul Mageed, Sidney Fels
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10 May 2019 - 8:57am
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[1] Pramit Saha, Muhammad Abdul Mageed, Sidney Fels, "DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4323. Accessed: Jul. 20, 2019.
@article{4323-19,
url = {http://sigport.org/4323},
author = {Pramit Saha; Muhammad Abdul Mageed; Sidney Fels },
publisher = {IEEE SigPort},
title = {DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS},
year = {2019} }
TY - EJOUR
T1 - DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS
AU - Pramit Saha; Muhammad Abdul Mageed; Sidney Fels
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4323
ER -
Pramit Saha, Muhammad Abdul Mageed, Sidney Fels. (2019). DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS. IEEE SigPort. http://sigport.org/4323
Pramit Saha, Muhammad Abdul Mageed, Sidney Fels, 2019. DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS. Available at: http://sigport.org/4323.
Pramit Saha, Muhammad Abdul Mageed, Sidney Fels. (2019). "DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS." Web.
1. Pramit Saha, Muhammad Abdul Mageed, Sidney Fels. DEEP LEARNING THE EEG MANIFOLD FOR PHONOLOGICAL CATEGORIZATION FROM ACTIVE THOUGHTS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4323

Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method


Road traffic forecasting systems are in scenarios where sensor or system failure occur. In those scenarios, it is known that missing values negatively affect estimation accuracy although it is being often underestimate in current deep neural network approaches. Our assumption is that traffic data can be generated from a latent space. Thus, we propose an online unsupervised data imputation method based on learning the data distribution using a variational autoencoder (VAE).

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Authors:
Guillem Boquet, Jose Lopez Vicario, Antoni Morell, Javier Serrano
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10 May 2019 - 7:11am
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[1] Guillem Boquet, Jose Lopez Vicario, Antoni Morell, Javier Serrano, "Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method ", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4303. Accessed: Jul. 20, 2019.
@article{4303-19,
url = {http://sigport.org/4303},
author = {Guillem Boquet; Jose Lopez Vicario; Antoni Morell; Javier Serrano },
publisher = {IEEE SigPort},
title = {Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method },
year = {2019} }
TY - EJOUR
T1 - Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method
AU - Guillem Boquet; Jose Lopez Vicario; Antoni Morell; Javier Serrano
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4303
ER -
Guillem Boquet, Jose Lopez Vicario, Antoni Morell, Javier Serrano. (2019). Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method . IEEE SigPort. http://sigport.org/4303
Guillem Boquet, Jose Lopez Vicario, Antoni Morell, Javier Serrano, 2019. Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method . Available at: http://sigport.org/4303.
Guillem Boquet, Jose Lopez Vicario, Antoni Morell, Javier Serrano. (2019). "Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method ." Web.
1. Guillem Boquet, Jose Lopez Vicario, Antoni Morell, Javier Serrano. Missing Data In Traffic Estimation: A Variational Autoencoder Imputation Method [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4303

Blind Room Volume Estimation from Single-Channel Noisy Speech


Recent work on acoustic parameter estimation indicates that geometric room volume can be useful for modeling the character of an acoustic environment. However, estimating volume from audio signals remains a challenging problem. Here we propose using a convolutional neural network model to estimate the room volume blindly from reverberant single-channel speech signals in the presence of noise. The model is shown to produce estimates within approximately a factor of two to the true value, for rooms ranging in size from small offices to large concert halls.

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Authors:
Andrea Genovese, Hannes Gamper, Ville Pullki, Nikunj Raghuvanshi, Ivan Tashev
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10 May 2019 - 2:44am
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[1] Andrea Genovese, Hannes Gamper, Ville Pullki, Nikunj Raghuvanshi, Ivan Tashev, "Blind Room Volume Estimation from Single-Channel Noisy Speech", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4277. Accessed: Jul. 20, 2019.
@article{4277-19,
url = {http://sigport.org/4277},
author = {Andrea Genovese; Hannes Gamper; Ville Pullki; Nikunj Raghuvanshi; Ivan Tashev },
publisher = {IEEE SigPort},
title = {Blind Room Volume Estimation from Single-Channel Noisy Speech},
year = {2019} }
TY - EJOUR
T1 - Blind Room Volume Estimation from Single-Channel Noisy Speech
AU - Andrea Genovese; Hannes Gamper; Ville Pullki; Nikunj Raghuvanshi; Ivan Tashev
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4277
ER -
Andrea Genovese, Hannes Gamper, Ville Pullki, Nikunj Raghuvanshi, Ivan Tashev. (2019). Blind Room Volume Estimation from Single-Channel Noisy Speech. IEEE SigPort. http://sigport.org/4277
Andrea Genovese, Hannes Gamper, Ville Pullki, Nikunj Raghuvanshi, Ivan Tashev, 2019. Blind Room Volume Estimation from Single-Channel Noisy Speech. Available at: http://sigport.org/4277.
Andrea Genovese, Hannes Gamper, Ville Pullki, Nikunj Raghuvanshi, Ivan Tashev. (2019). "Blind Room Volume Estimation from Single-Channel Noisy Speech." Web.
1. Andrea Genovese, Hannes Gamper, Ville Pullki, Nikunj Raghuvanshi, Ivan Tashev. Blind Room Volume Estimation from Single-Channel Noisy Speech [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4277

Generative Graph Convolutional Network For Growing Graphs

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Authors:
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
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9 May 2019 - 8:38pm
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[1] Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan, "Generative Graph Convolutional Network For Growing Graphs", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4247. Accessed: Jul. 20, 2019.
@article{4247-19,
url = {http://sigport.org/4247},
author = {Da Xu; Chuanwei Ruan; Evren Korpeoglu; Sushant Kumar; Kannan Achan },
publisher = {IEEE SigPort},
title = {Generative Graph Convolutional Network For Growing Graphs},
year = {2019} }
TY - EJOUR
T1 - Generative Graph Convolutional Network For Growing Graphs
AU - Da Xu; Chuanwei Ruan; Evren Korpeoglu; Sushant Kumar; Kannan Achan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4247
ER -
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan. (2019). Generative Graph Convolutional Network For Growing Graphs. IEEE SigPort. http://sigport.org/4247
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan, 2019. Generative Graph Convolutional Network For Growing Graphs. Available at: http://sigport.org/4247.
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan. (2019). "Generative Graph Convolutional Network For Growing Graphs." Web.
1. Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan. Generative Graph Convolutional Network For Growing Graphs [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4247

DSSLIC: Deep Semantic Segmentation-based Layered Image Compression


We propose a deep semantic segmentation-based layered image compression (DSSLIC) framework in which the segmentation map of the input image is obtained and encoded as the base layer of the bit-stream. Experimental results show that the proposed framework outperforms the H.265/HEVC-based BPG and other codecs in both PSNR and MS-SSIM metrics in RGB domain. Besides, since semantic map is included in the bit-stream, the proposed scheme can facilitate many other tasks such as image search and object-based adaptive image compression.

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Authors:
Jie Liang, Jingning Han
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9 May 2019 - 4:16pm
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[1] Jie Liang, Jingning Han, "DSSLIC: Deep Semantic Segmentation-based Layered Image Compression", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4236. Accessed: Jul. 20, 2019.
@article{4236-19,
url = {http://sigport.org/4236},
author = {Jie Liang; Jingning Han },
publisher = {IEEE SigPort},
title = {DSSLIC: Deep Semantic Segmentation-based Layered Image Compression},
year = {2019} }
TY - EJOUR
T1 - DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
AU - Jie Liang; Jingning Han
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4236
ER -
Jie Liang, Jingning Han. (2019). DSSLIC: Deep Semantic Segmentation-based Layered Image Compression. IEEE SigPort. http://sigport.org/4236
Jie Liang, Jingning Han, 2019. DSSLIC: Deep Semantic Segmentation-based Layered Image Compression. Available at: http://sigport.org/4236.
Jie Liang, Jingning Han. (2019). "DSSLIC: Deep Semantic Segmentation-based Layered Image Compression." Web.
1. Jie Liang, Jingning Han. DSSLIC: Deep Semantic Segmentation-based Layered Image Compression [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4236

Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration


Special high-end sensors with expensive hardware are usually needed to measure shock signals with high accuracy. In this paper, we show that cheap low-end sensors calibrated by deep neural networks are also capable to measure high-g shocks accurately. Firstly we perform drop shock tests to collect a dataset of shock signals measured by sensors of different fidelity. Secondly, we propose a novel network to effectively learn both the signal peak and overall shape.

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Authors:
Houpu Yao, Jingjing Wen, Yi Ren, Bin Wu, Ze Ji
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9 May 2019 - 9:30am
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[1] Houpu Yao, Jingjing Wen, Yi Ren, Bin Wu, Ze Ji, "Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4199. Accessed: Jul. 20, 2019.
@article{4199-19,
url = {http://sigport.org/4199},
author = {Houpu Yao; Jingjing Wen; Yi Ren; Bin Wu; Ze Ji },
publisher = {IEEE SigPort},
title = {Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration},
year = {2019} }
TY - EJOUR
T1 - Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration
AU - Houpu Yao; Jingjing Wen; Yi Ren; Bin Wu; Ze Ji
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4199
ER -
Houpu Yao, Jingjing Wen, Yi Ren, Bin Wu, Ze Ji. (2019). Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration. IEEE SigPort. http://sigport.org/4199
Houpu Yao, Jingjing Wen, Yi Ren, Bin Wu, Ze Ji, 2019. Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration. Available at: http://sigport.org/4199.
Houpu Yao, Jingjing Wen, Yi Ren, Bin Wu, Ze Ji. (2019). "Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration." Web.
1. Houpu Yao, Jingjing Wen, Yi Ren, Bin Wu, Ze Ji. Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4199

Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection


In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty detection. By integrating uncertainty into the hidden states, this type of network is able to learn the distribution of complex sequences. Because the learned distribution can be calculated explicitly in terms of probability, we can evaluate how likely an observation is then detect low-probability events as novel.

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Authors:
Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin
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9 May 2019 - 6:26am
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[1] Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin, "Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4185. Accessed: Jul. 20, 2019.
@article{4185-19,
url = {http://sigport.org/4185},
author = {Duong Nguyen ; Oliver S. Kirsebom ; Fábio Frazão ; Ronan Fablet ; Stan Matwin },
publisher = {IEEE SigPort},
title = {Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection},
year = {2019} }
TY - EJOUR
T1 - Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
AU - Duong Nguyen ; Oliver S. Kirsebom ; Fábio Frazão ; Ronan Fablet ; Stan Matwin
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4185
ER -
Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin. (2019). Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection. IEEE SigPort. http://sigport.org/4185
Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin, 2019. Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection. Available at: http://sigport.org/4185.
Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin. (2019). "Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection." Web.
1. Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin. Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4185

LEARNING TEMPORAL INFORMATION FROM SPATIAL INFORMATION USING CAPSNETS FOR HUMAN ACTION RECOGNITION


Capsule Networks (CapsNets) are recently introduced to overcome some of the shortcomings of traditional Convolutional Neural Networks (CNNs). CapsNets replace neurons in CNNs with vectors to retain spatial relationships among the features. In this paper, we propose a CapsNet architecture that employs individual video frames for human action recognition without explicitly extracting motion information. We also propose weight pooling to reduce the computational complexity and improve the classification accuracy by appropriately removing some of the extracted features.

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Authors:
Abdullah M. Algamdi , Victor Sanchez , Chang-Tsun Li
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8 May 2019 - 8:03am
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[1] Abdullah M. Algamdi , Victor Sanchez , Chang-Tsun Li, "LEARNING TEMPORAL INFORMATION FROM SPATIAL INFORMATION USING CAPSNETS FOR HUMAN ACTION RECOGNITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4079. Accessed: Jul. 20, 2019.
@article{4079-19,
url = {http://sigport.org/4079},
author = {Abdullah M. Algamdi ; Victor Sanchez ; Chang-Tsun Li },
publisher = {IEEE SigPort},
title = {LEARNING TEMPORAL INFORMATION FROM SPATIAL INFORMATION USING CAPSNETS FOR HUMAN ACTION RECOGNITION},
year = {2019} }
TY - EJOUR
T1 - LEARNING TEMPORAL INFORMATION FROM SPATIAL INFORMATION USING CAPSNETS FOR HUMAN ACTION RECOGNITION
AU - Abdullah M. Algamdi ; Victor Sanchez ; Chang-Tsun Li
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4079
ER -
Abdullah M. Algamdi , Victor Sanchez , Chang-Tsun Li. (2019). LEARNING TEMPORAL INFORMATION FROM SPATIAL INFORMATION USING CAPSNETS FOR HUMAN ACTION RECOGNITION. IEEE SigPort. http://sigport.org/4079
Abdullah M. Algamdi , Victor Sanchez , Chang-Tsun Li, 2019. LEARNING TEMPORAL INFORMATION FROM SPATIAL INFORMATION USING CAPSNETS FOR HUMAN ACTION RECOGNITION. Available at: http://sigport.org/4079.
Abdullah M. Algamdi , Victor Sanchez , Chang-Tsun Li. (2019). "LEARNING TEMPORAL INFORMATION FROM SPATIAL INFORMATION USING CAPSNETS FOR HUMAN ACTION RECOGNITION." Web.
1. Abdullah M. Algamdi , Victor Sanchez , Chang-Tsun Li. LEARNING TEMPORAL INFORMATION FROM SPATIAL INFORMATION USING CAPSNETS FOR HUMAN ACTION RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4079

Hierarchy-aware Loss Function on a Tree Structured Label Space for Audio Event Detection


The paper introduces a hierarchy-aware loss function in a Deep Neural Network for an audio event detection task that has a bi-level tree structured label space. The goal is not only to improve audio event detection performance at all levels in the label hierarchy, but also to produce better audio embeddings. We exploit the label tree structure to preserve that information in the hierarchy-aware loss function. Two different loss functions are separately employed. First, a triplet loss with probabilistic multi-level batch mining is introduced.

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Authors:
Arindam Jati, Naveen Kumar, Ruxin Chen, Panayiotis Georgiou
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10 May 2019 - 1:02am
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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/4060. Accessed: Jul. 20, 2019.
@article{4060-19,
url = {http://sigport.org/4060},
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/4060
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/4060
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/4060.
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/4060

ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS


The cloud-based speech recognition/API provides developers or enterprises an easy way to create speech-enabled features in their applications. However, sending audios about personal or company internal information to the cloud, raises concerns about the privacy and security issues. The recognition results generated in cloud may also reveal some sensitive information. This paper proposes a deep polynomial network (DPN) that can be applied to the encrypted speech as an acoustic model. It allows clients to send their data in an encrypted form to the cloud to ensure that their data remains confidential, at mean while the DPN can still make frame-level predictions over the encrypted speech and return them in encrypted form. One good property of the DPN is that it can be trained on unencrypted speech features in the traditional way. To keep the cloud away from the raw audio and recognition results, a cloud-local joint decoding framework is also proposed. We demonstrate the effectiveness of model and framework on the Switchboard and Cortana voice assistant tasks with small performance degradation and latency increased comparing with the traditional cloud-based DNNs.
https://ieeexplore.ieee.org/document/8683721

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Authors:
Yifan Gong, Dong YU
Submitted On:
8 May 2019 - 4:21am
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[1] Yifan Gong, Dong YU, " ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4045. Accessed: Jul. 20, 2019.
@article{4045-19,
url = {http://sigport.org/4045},
author = {Yifan Gong; Dong YU },
publisher = {IEEE SigPort},
title = { ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS
AU - Yifan Gong; Dong YU
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4045
ER -
Yifan Gong, Dong YU. (2019). ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS. IEEE SigPort. http://sigport.org/4045
Yifan Gong, Dong YU, 2019. ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS. Available at: http://sigport.org/4045.
Yifan Gong, Dong YU. (2019). " ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS." Web.
1. Yifan Gong, Dong YU. ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4045

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