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ICASSP 2020

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 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.

An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers


As automatic speaker recognizer systems become mainstream, voice spoofing attacks are on the rise. Common attack strategies include replay, the use of text-to-speech synthesis, and voice conversion systems. While previously-proposed end-to-end detection frameworks have shown to be effective in spotting attacks for one particular spoofing strategy, they have relied on different models, architectures, and speech representations, depending on the spoofing strategy.

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Authors:
Jahangir Alam,Tiago Falk
Submitted On:
13 May 2020 - 5:21pm
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ICASSP_Spoofing.pdf

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[1] Jahangir Alam,Tiago Falk, "An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5149. Accessed: Aug. 06, 2020.
@article{5149-20,
url = {http://sigport.org/5149},
author = {Jahangir Alam;Tiago Falk },
publisher = {IEEE SigPort},
title = {An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers},
year = {2020} }
TY - EJOUR
T1 - An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers
AU - Jahangir Alam;Tiago Falk
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5149
ER -
Jahangir Alam,Tiago Falk. (2020). An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers. IEEE SigPort. http://sigport.org/5149
Jahangir Alam,Tiago Falk, 2020. An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers. Available at: http://sigport.org/5149.
Jahangir Alam,Tiago Falk. (2020). "An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers." Web.
1. Jahangir Alam,Tiago Falk. An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5149

STRUCTURAL SPARSIFICATION FOR FAR-FIELD SPEAKER RECOGNITION WITH INTEL GNA

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Authors:
Jingchi Zhang, Jonathan Huang, Mike Deisher, Hai Li, Yiran Chen
Submitted On:
13 May 2020 - 5:20pm
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[1] Jingchi Zhang, Jonathan Huang, Mike Deisher, Hai Li, Yiran Chen, "STRUCTURAL SPARSIFICATION FOR FAR-FIELD SPEAKER RECOGNITION WITH INTEL GNA", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5148. Accessed: Aug. 06, 2020.
@article{5148-20,
url = {http://sigport.org/5148},
author = {Jingchi Zhang; Jonathan Huang; Mike Deisher; Hai Li; Yiran Chen },
publisher = {IEEE SigPort},
title = {STRUCTURAL SPARSIFICATION FOR FAR-FIELD SPEAKER RECOGNITION WITH INTEL GNA},
year = {2020} }
TY - EJOUR
T1 - STRUCTURAL SPARSIFICATION FOR FAR-FIELD SPEAKER RECOGNITION WITH INTEL GNA
AU - Jingchi Zhang; Jonathan Huang; Mike Deisher; Hai Li; Yiran Chen
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5148
ER -
Jingchi Zhang, Jonathan Huang, Mike Deisher, Hai Li, Yiran Chen. (2020). STRUCTURAL SPARSIFICATION FOR FAR-FIELD SPEAKER RECOGNITION WITH INTEL GNA. IEEE SigPort. http://sigport.org/5148
Jingchi Zhang, Jonathan Huang, Mike Deisher, Hai Li, Yiran Chen, 2020. STRUCTURAL SPARSIFICATION FOR FAR-FIELD SPEAKER RECOGNITION WITH INTEL GNA. Available at: http://sigport.org/5148.
Jingchi Zhang, Jonathan Huang, Mike Deisher, Hai Li, Yiran Chen. (2020). "STRUCTURAL SPARSIFICATION FOR FAR-FIELD SPEAKER RECOGNITION WITH INTEL GNA." Web.
1. Jingchi Zhang, Jonathan Huang, Mike Deisher, Hai Li, Yiran Chen. STRUCTURAL SPARSIFICATION FOR FAR-FIELD SPEAKER RECOGNITION WITH INTEL GNA [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5148

Information Flow Optimization in Inference Networks


The problem of maximizing the information flow through a sensor network tasked with an inference objective at the fusion center is considered. The sensor nodes take observations, compress, and send them to the fusion center through a network of relays. The network imposes capacity constraints on the rate of transmission in each connection and flow conservation constraints. It is shown that this rate-constrained inference problem can be cast as a Network Utility Maximization problem by suitably defining the utility functions for each sensor, and can be solved using existing techniques.

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Authors:
Jing Liu, Venugopal Veeravalli, Gunjan Verma
Submitted On:
13 May 2020 - 5:16pm
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[1] Jing Liu, Venugopal Veeravalli, Gunjan Verma, "Information Flow Optimization in Inference Networks", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5147. Accessed: Aug. 06, 2020.
@article{5147-20,
url = {http://sigport.org/5147},
author = {Jing Liu; Venugopal Veeravalli; Gunjan Verma },
publisher = {IEEE SigPort},
title = {Information Flow Optimization in Inference Networks},
year = {2020} }
TY - EJOUR
T1 - Information Flow Optimization in Inference Networks
AU - Jing Liu; Venugopal Veeravalli; Gunjan Verma
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5147
ER -
Jing Liu, Venugopal Veeravalli, Gunjan Verma. (2020). Information Flow Optimization in Inference Networks. IEEE SigPort. http://sigport.org/5147
Jing Liu, Venugopal Veeravalli, Gunjan Verma, 2020. Information Flow Optimization in Inference Networks. Available at: http://sigport.org/5147.
Jing Liu, Venugopal Veeravalli, Gunjan Verma. (2020). "Information Flow Optimization in Inference Networks." Web.
1. Jing Liu, Venugopal Veeravalli, Gunjan Verma. Information Flow Optimization in Inference Networks [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5147

BBAND Index: a No-Reference Banding Artifact Predictor


Banding artifact, or false contouring, is a common video compression impairment that tends to appear on large flat regions in encoded videos. These staircase-shaped color bands can be very noticeable in high-definition videos. Here we study this artifact, and propose a new distortion-specific no-reference video quality model for predicting banding artifacts, called the Blind BANding Detector (BBAND index). BBAND is inspired by human visual models. The proposed detector can generate a pixel-wise banding visibility map and output a banding severity score at both the frame and video levels.

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Authors:
Zhengzhong Tu, Jessie Lin, Yilin Wang, Balu Adsumilli, and Alan Bovik
Submitted On:
13 May 2020 - 5:18pm
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[1] Zhengzhong Tu, Jessie Lin, Yilin Wang, Balu Adsumilli, and Alan Bovik, "BBAND Index: a No-Reference Banding Artifact Predictor", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5146. Accessed: Aug. 06, 2020.
@article{5146-20,
url = {http://sigport.org/5146},
author = {Zhengzhong Tu; Jessie Lin; Yilin Wang; Balu Adsumilli; and Alan Bovik },
publisher = {IEEE SigPort},
title = {BBAND Index: a No-Reference Banding Artifact Predictor},
year = {2020} }
TY - EJOUR
T1 - BBAND Index: a No-Reference Banding Artifact Predictor
AU - Zhengzhong Tu; Jessie Lin; Yilin Wang; Balu Adsumilli; and Alan Bovik
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5146
ER -
Zhengzhong Tu, Jessie Lin, Yilin Wang, Balu Adsumilli, and Alan Bovik. (2020). BBAND Index: a No-Reference Banding Artifact Predictor. IEEE SigPort. http://sigport.org/5146
Zhengzhong Tu, Jessie Lin, Yilin Wang, Balu Adsumilli, and Alan Bovik, 2020. BBAND Index: a No-Reference Banding Artifact Predictor. Available at: http://sigport.org/5146.
Zhengzhong Tu, Jessie Lin, Yilin Wang, Balu Adsumilli, and Alan Bovik. (2020). "BBAND Index: a No-Reference Banding Artifact Predictor." Web.
1. Zhengzhong Tu, Jessie Lin, Yilin Wang, Balu Adsumilli, and Alan Bovik. BBAND Index: a No-Reference Banding Artifact Predictor [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5146

Meta Learning for Robust Child/Adult Classification from Speech


Computational modeling of naturalistic conversations in clinical applications has seen growing interest in the past decade. An important use-case involves child-adult interactions within the autism diagnosis and intervention domain. In this paper, we address a specific sub-problem of speaker diarization, namely child-adult speaker classification in such dyadic conversations with specified roles. Training a speaker classification system robust to speaker and channel conditions is challenging due to inherent variability in the speech within children and the adult interlocutors.

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Authors:
Nithin Rao Koluguri, Manoj Kumar, So Hyun Kim, Catherine Lord, Shrikanth Narayanan
Submitted On:
13 May 2020 - 5:07pm
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metaLearning_slides.pdf

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[1] Nithin Rao Koluguri, Manoj Kumar, So Hyun Kim, Catherine Lord, Shrikanth Narayanan, "Meta Learning for Robust Child/Adult Classification from Speech", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5145. Accessed: Aug. 06, 2020.
@article{5145-20,
url = {http://sigport.org/5145},
author = {Nithin Rao Koluguri; Manoj Kumar; So Hyun Kim; Catherine Lord; Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {Meta Learning for Robust Child/Adult Classification from Speech},
year = {2020} }
TY - EJOUR
T1 - Meta Learning for Robust Child/Adult Classification from Speech
AU - Nithin Rao Koluguri; Manoj Kumar; So Hyun Kim; Catherine Lord; Shrikanth Narayanan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5145
ER -
Nithin Rao Koluguri, Manoj Kumar, So Hyun Kim, Catherine Lord, Shrikanth Narayanan. (2020). Meta Learning for Robust Child/Adult Classification from Speech. IEEE SigPort. http://sigport.org/5145
Nithin Rao Koluguri, Manoj Kumar, So Hyun Kim, Catherine Lord, Shrikanth Narayanan, 2020. Meta Learning for Robust Child/Adult Classification from Speech. Available at: http://sigport.org/5145.
Nithin Rao Koluguri, Manoj Kumar, So Hyun Kim, Catherine Lord, Shrikanth Narayanan. (2020). "Meta Learning for Robust Child/Adult Classification from Speech." Web.
1. Nithin Rao Koluguri, Manoj Kumar, So Hyun Kim, Catherine Lord, Shrikanth Narayanan. Meta Learning for Robust Child/Adult Classification from Speech [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5145

STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS


Smart grids are faced with the challenge of meeting the ever increasing load demands of electric vehicles (EVs). To provide acceptable charging services, operators need to be equipped with an efficient charging stations (CSs) planning strategy. Unfortunately, existing planning solutions are quite limited. They normally rely on standard IEEE bus systems or power grids that are specific to certain cities. In this paper, using stochastic geometry, we formulate the CSs planning on a stochastic geometry-based power grid model, that we previously showed to mimic real-world power grids.

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Authors:
Rachad Atat, Muhammad Ismail, and Erchin Serpedin
Submitted On:
13 May 2020 - 5:04pm
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Presentation_ICASSP_2020.pdf

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[1] Rachad Atat, Muhammad Ismail, and Erchin Serpedin, "STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5144. Accessed: Aug. 06, 2020.
@article{5144-20,
url = {http://sigport.org/5144},
author = {Rachad Atat; Muhammad Ismail; and Erchin Serpedin },
publisher = {IEEE SigPort},
title = {STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS},
year = {2020} }
TY - EJOUR
T1 - STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS
AU - Rachad Atat; Muhammad Ismail; and Erchin Serpedin
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5144
ER -
Rachad Atat, Muhammad Ismail, and Erchin Serpedin. (2020). STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS. IEEE SigPort. http://sigport.org/5144
Rachad Atat, Muhammad Ismail, and Erchin Serpedin, 2020. STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS. Available at: http://sigport.org/5144.
Rachad Atat, Muhammad Ismail, and Erchin Serpedin. (2020). "STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS." Web.
1. Rachad Atat, Muhammad Ismail, and Erchin Serpedin. STOCHASTIC GEOMETRY PLANNING OF ELECTRIC VEHICLES CHARGING STATIONS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5144

Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer


Many portable imaging devices use the operation of “trunc” (rounding towards zero) instead of rounding as the final quantizer for computing DCT coefficients during JPEG compression. We show that this has rather profound consequences for steganography and its detection. In particular, side-informed steganography needs to be redesigned due to the different nature of the rounding error. The steganographic algorithm J-UNIWARD becomes vulnerable to steganalysis with the JPEG rich model and needs to be adjusted for this source.

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Authors:
Jan Butora, Jessica Fridrich
Submitted On:
13 May 2020 - 5:02pm
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[1] Jan Butora, Jessica Fridrich, "Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5143. Accessed: Aug. 06, 2020.
@article{5143-20,
url = {http://sigport.org/5143},
author = {Jan Butora; Jessica Fridrich },
publisher = {IEEE SigPort},
title = {Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer},
year = {2020} }
TY - EJOUR
T1 - Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer
AU - Jan Butora; Jessica Fridrich
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5143
ER -
Jan Butora, Jessica Fridrich. (2020). Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer. IEEE SigPort. http://sigport.org/5143
Jan Butora, Jessica Fridrich, 2020. Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer. Available at: http://sigport.org/5143.
Jan Butora, Jessica Fridrich. (2020). "Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer." Web.
1. Jan Butora, Jessica Fridrich. Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5143

Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides

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Authors:
Mircea Moscu, Ricardo Borsoi, Cédric Richard
Submitted On:
13 May 2020 - 4:54pm
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slides for the video presentation

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[1] Mircea Moscu, Ricardo Borsoi, Cédric Richard, "Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5141. Accessed: Aug. 06, 2020.
@article{5141-20,
url = {http://sigport.org/5141},
author = {Mircea Moscu; Ricardo Borsoi; Cédric Richard },
publisher = {IEEE SigPort},
title = {Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides},
year = {2020} }
TY - EJOUR
T1 - Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides
AU - Mircea Moscu; Ricardo Borsoi; Cédric Richard
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5141
ER -
Mircea Moscu, Ricardo Borsoi, Cédric Richard. (2020). Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides. IEEE SigPort. http://sigport.org/5141
Mircea Moscu, Ricardo Borsoi, Cédric Richard, 2020. Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides. Available at: http://sigport.org/5141.
Mircea Moscu, Ricardo Borsoi, Cédric Richard. (2020). "Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides." Web.
1. Mircea Moscu, Ricardo Borsoi, Cédric Richard. Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5141

Node-Asynchronous Spectral Clustering On Directed Graphs

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Authors:
Oguzhan Teke, Palghat P. Vaidyanathan
Submitted On:
13 May 2020 - 4:50pm
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[1] Oguzhan Teke, Palghat P. Vaidyanathan, "Node-Asynchronous Spectral Clustering On Directed Graphs", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5140. Accessed: Aug. 06, 2020.
@article{5140-20,
url = {http://sigport.org/5140},
author = {Oguzhan Teke; Palghat P. Vaidyanathan },
publisher = {IEEE SigPort},
title = {Node-Asynchronous Spectral Clustering On Directed Graphs},
year = {2020} }
TY - EJOUR
T1 - Node-Asynchronous Spectral Clustering On Directed Graphs
AU - Oguzhan Teke; Palghat P. Vaidyanathan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5140
ER -
Oguzhan Teke, Palghat P. Vaidyanathan. (2020). Node-Asynchronous Spectral Clustering On Directed Graphs. IEEE SigPort. http://sigport.org/5140
Oguzhan Teke, Palghat P. Vaidyanathan, 2020. Node-Asynchronous Spectral Clustering On Directed Graphs. Available at: http://sigport.org/5140.
Oguzhan Teke, Palghat P. Vaidyanathan. (2020). "Node-Asynchronous Spectral Clustering On Directed Graphs." Web.
1. Oguzhan Teke, Palghat P. Vaidyanathan. Node-Asynchronous Spectral Clustering On Directed Graphs [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5140

Speaker-aware Training of Attention-based End-to-End Speech Recognition using Neural Speaker Embeddings


In speaker-aware training, a speaker embedding is appended to DNN input features. This allows the DNN to effectively learn representations, which are robust to speaker variability.
We apply speaker-aware training to attention-based end- to-end speech recognition. We show that it can improve over a purely end-to-end baseline. We also propose speaker-aware training as a viable method to leverage untranscribed, speaker annotated data.

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Authors:
Aku Rouhe, Tuomas Kaseva, Mikko Kurimo
Submitted On:
13 May 2020 - 4:49pm
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[1] Aku Rouhe, Tuomas Kaseva, Mikko Kurimo, "Speaker-aware Training of Attention-based End-to-End Speech Recognition using Neural Speaker Embeddings", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5139. Accessed: Aug. 06, 2020.
@article{5139-20,
url = {http://sigport.org/5139},
author = {Aku Rouhe; Tuomas Kaseva; Mikko Kurimo },
publisher = {IEEE SigPort},
title = {Speaker-aware Training of Attention-based End-to-End Speech Recognition using Neural Speaker Embeddings},
year = {2020} }
TY - EJOUR
T1 - Speaker-aware Training of Attention-based End-to-End Speech Recognition using Neural Speaker Embeddings
AU - Aku Rouhe; Tuomas Kaseva; Mikko Kurimo
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5139
ER -
Aku Rouhe, Tuomas Kaseva, Mikko Kurimo. (2020). Speaker-aware Training of Attention-based End-to-End Speech Recognition using Neural Speaker Embeddings. IEEE SigPort. http://sigport.org/5139
Aku Rouhe, Tuomas Kaseva, Mikko Kurimo, 2020. Speaker-aware Training of Attention-based End-to-End Speech Recognition using Neural Speaker Embeddings. Available at: http://sigport.org/5139.
Aku Rouhe, Tuomas Kaseva, Mikko Kurimo. (2020). "Speaker-aware Training of Attention-based End-to-End Speech Recognition using Neural Speaker Embeddings." Web.
1. Aku Rouhe, Tuomas Kaseva, Mikko Kurimo. Speaker-aware Training of Attention-based End-to-End Speech Recognition using Neural Speaker Embeddings [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5139

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