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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

Adversarial Speaker Adaptation


We propose a novel adversarial speaker adaptation (ASA) scheme, in which adversarial learning is applied to regularize the distribution of deep hidden features in a speaker-dependent (SD) deep neural network (DNN) acoustic model to be close to that of a fixed speaker-independent (SI) DNN acoustic model during adaptation. An additional discriminator network is introduced to distinguish the deep features generated by the SD model from those produced by the SI model.

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Authors:
Zhong Meng, Jinyu Li, Yifan Gong
Submitted On:
12 May 2019 - 9:26pm
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[1] Zhong Meng, Jinyu Li, Yifan Gong, "Adversarial Speaker Adaptation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4475. Accessed: Sep. 21, 2019.
@article{4475-19,
url = {http://sigport.org/4475},
author = {Zhong Meng; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Adversarial Speaker Adaptation},
year = {2019} }
TY - EJOUR
T1 - Adversarial Speaker Adaptation
AU - Zhong Meng; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4475
ER -
Zhong Meng, Jinyu Li, Yifan Gong. (2019). Adversarial Speaker Adaptation. IEEE SigPort. http://sigport.org/4475
Zhong Meng, Jinyu Li, Yifan Gong, 2019. Adversarial Speaker Adaptation. Available at: http://sigport.org/4475.
Zhong Meng, Jinyu Li, Yifan Gong. (2019). "Adversarial Speaker Adaptation." Web.
1. Zhong Meng, Jinyu Li, Yifan Gong. Adversarial Speaker Adaptation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4475

Attentive Adversarial Learning for Domain-Invariant Training


Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network (DNN) acoustic model and is trained to improve their domain-invariance by optimizing an adversarial loss function.

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Authors:
Zhong Meng, Jinyu Li, Yifan Gong
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12 May 2019 - 9:03pm
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[1] Zhong Meng, Jinyu Li, Yifan Gong, "Attentive Adversarial Learning for Domain-Invariant Training", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4474. Accessed: Sep. 21, 2019.
@article{4474-19,
url = {http://sigport.org/4474},
author = {Zhong Meng; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Attentive Adversarial Learning for Domain-Invariant Training},
year = {2019} }
TY - EJOUR
T1 - Attentive Adversarial Learning for Domain-Invariant Training
AU - Zhong Meng; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4474
ER -
Zhong Meng, Jinyu Li, Yifan Gong. (2019). Attentive Adversarial Learning for Domain-Invariant Training. IEEE SigPort. http://sigport.org/4474
Zhong Meng, Jinyu Li, Yifan Gong, 2019. Attentive Adversarial Learning for Domain-Invariant Training. Available at: http://sigport.org/4474.
Zhong Meng, Jinyu Li, Yifan Gong. (2019). "Attentive Adversarial Learning for Domain-Invariant Training." Web.
1. Zhong Meng, Jinyu Li, Yifan Gong. Attentive Adversarial Learning for Domain-Invariant Training [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4474

Adversarial Speaker Verification


The use of deep networks to extract embeddings for speaker recognition has proven successfully. However, such embeddings are susceptible to performance degradation due to the mismatches among the training, enrollment, and test conditions. In this work, we propose an adversarial speaker verification (ASV) scheme to learn the condition-invariant deep embedding via adversarial multi-task training. In ASV, a speaker classification network and a condition identification network are jointly optimized to minimize the speaker classification loss and simultaneously mini-maximize the condition loss.

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Authors:
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong
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12 May 2019 - 9:24pm
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[1] Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong, "Adversarial Speaker Verification", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4473. Accessed: Sep. 21, 2019.
@article{4473-19,
url = {http://sigport.org/4473},
author = {Zhong Meng; Yong Zhao; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Adversarial Speaker Verification},
year = {2019} }
TY - EJOUR
T1 - Adversarial Speaker Verification
AU - Zhong Meng; Yong Zhao; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4473
ER -
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. (2019). Adversarial Speaker Verification. IEEE SigPort. http://sigport.org/4473
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong, 2019. Adversarial Speaker Verification. Available at: http://sigport.org/4473.
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. (2019). "Adversarial Speaker Verification." Web.
1. Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. Adversarial Speaker Verification [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4473

Conditional Teacher-Student Learning


The teacher-student (T/S) learning has been shown to be effective for a variety of problems such as domain adaptation and model compression. One shortcoming of the T/S learning is that a teacher model, not always perfect, sporadically produces wrong guidance in form of posterior probabilities that misleads the student model towards a suboptimal performance.

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Authors:
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong
Submitted On:
12 May 2019 - 9:23pm
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cts_poster.pptx

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[1] Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong, "Conditional Teacher-Student Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4472. Accessed: Sep. 21, 2019.
@article{4472-19,
url = {http://sigport.org/4472},
author = {Zhong Meng; Jinyu Li; Yong Zhao; Yifan Gong },
publisher = {IEEE SigPort},
title = {Conditional Teacher-Student Learning},
year = {2019} }
TY - EJOUR
T1 - Conditional Teacher-Student Learning
AU - Zhong Meng; Jinyu Li; Yong Zhao; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4472
ER -
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. (2019). Conditional Teacher-Student Learning. IEEE SigPort. http://sigport.org/4472
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong, 2019. Conditional Teacher-Student Learning. Available at: http://sigport.org/4472.
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. (2019). "Conditional Teacher-Student Learning." Web.
1. Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. Conditional Teacher-Student Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4472

QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL


The performance of voice-based systems for remote monitoring of Parkinson’s disease is highly dependent on the degree of adherence of the recordings to the test protocols, which probe for specific symptoms. Identifying segments of the signal that adhere to the protocol assumptions is typically performed manually by experts. This process is costly, time consuming, and often infeasible for large-scale data sets. In this paper, we propose a method to automatically identify the segments of signals that violate the test protocol with a high accuracy.

Paper Details

Authors:
Amir Hossein Poorjam, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little
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12 May 2019 - 6:13pm
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Infinite hidden Markov model for quality control

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[1] Amir Hossein Poorjam, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little, "QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4471. Accessed: Sep. 21, 2019.
@article{4471-19,
url = {http://sigport.org/4471},
author = {Amir Hossein Poorjam; Yordan P. Raykov; Reham Badawy; Jesper Rindom Jensen; Mads Græsbøll Christensen; Max A. Little },
publisher = {IEEE SigPort},
title = {QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL},
year = {2019} }
TY - EJOUR
T1 - QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL
AU - Amir Hossein Poorjam; Yordan P. Raykov; Reham Badawy; Jesper Rindom Jensen; Mads Græsbøll Christensen; Max A. Little
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4471
ER -
Amir Hossein Poorjam, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little. (2019). QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL. IEEE SigPort. http://sigport.org/4471
Amir Hossein Poorjam, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little, 2019. QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL. Available at: http://sigport.org/4471.
Amir Hossein Poorjam, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little. (2019). "QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL." Web.
1. Amir Hossein Poorjam, Yordan P. Raykov, Reham Badawy, Jesper Rindom Jensen, Mads Græsbøll Christensen, Max A. Little. QUALITY CONTROL OF VOICE RECORDINGS IN REMOTE PARKINSON'S DISEASE MONITORING USING THE INFINITE HIDDEN MARKOV MODEL [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4471

ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES

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Authors:
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang
Submitted On:
12 May 2019 - 4:02pm
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[1] Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang, "ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4470. Accessed: Sep. 21, 2019.
@article{4470-19,
url = {http://sigport.org/4470},
author = {Karim Armanious; Youssef Mecky; Sergios Gatidis; Bin Yang },
publisher = {IEEE SigPort},
title = {ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES},
year = {2019} }
TY - EJOUR
T1 - ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES
AU - Karim Armanious; Youssef Mecky; Sergios Gatidis; Bin Yang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4470
ER -
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang. (2019). ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES. IEEE SigPort. http://sigport.org/4470
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang, 2019. ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES. Available at: http://sigport.org/4470.
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang. (2019). "ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES." Web.
1. Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang. ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4470

TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION


We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace. This is a matched subspace detection problem. Tensor matched subspace detection problem is more challenging because of the intertwined signal dimensions. We solve this problem by projecting the signal onto the KS subspace, which is a Kronecker product of different subspaces corresponding to each signal dimension. Under this framework, we define the KS subspaces and the orthogonal projection of the signal onto the KS subspace.

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Authors:
Ishan Jindal, Matthew Nokleby
Submitted On:
12 May 2019 - 1:39pm
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Poster for the paper titled TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION

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[1] Ishan Jindal, Matthew Nokleby, "TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4468. Accessed: Sep. 21, 2019.
@article{4468-19,
url = {http://sigport.org/4468},
author = {Ishan Jindal; Matthew Nokleby },
publisher = {IEEE SigPort},
title = {TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION},
year = {2019} }
TY - EJOUR
T1 - TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION
AU - Ishan Jindal; Matthew Nokleby
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4468
ER -
Ishan Jindal, Matthew Nokleby. (2019). TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION. IEEE SigPort. http://sigport.org/4468
Ishan Jindal, Matthew Nokleby, 2019. TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION. Available at: http://sigport.org/4468.
Ishan Jindal, Matthew Nokleby. (2019). "TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION." Web.
1. Ishan Jindal, Matthew Nokleby. TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4468

FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES


Deep learning models have significantly improved the visual quality and accuracy on compressive sensing recovery. In this paper, we propose an algorithm for signal reconstruction from compressed measurements with image priors captured by a generative model. We search and constrain on latent variable space to make the method stable when the number of compressed measurements is extremely limited. We show that, by exploiting certain structures of the latent variables, the proposed method produces improved reconstruction accuracy and preserves realistic and non-smooth features in the image.

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12 May 2019 - 12:59pm
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Xu, Shaojie ICCASP 2019 Presentation Slides.pdf

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[1] , "FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4467. Accessed: Sep. 21, 2019.
@article{4467-19,
url = {http://sigport.org/4467},
author = { },
publisher = {IEEE SigPort},
title = {FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES},
year = {2019} }
TY - EJOUR
T1 - FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4467
ER -
. (2019). FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES. IEEE SigPort. http://sigport.org/4467
, 2019. FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES. Available at: http://sigport.org/4467.
. (2019). "FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES." Web.
1. . FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4467

ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION


Fluent and confident speech is desirable to every speaker. But professional speech delivering requires a great deal of experience and practice. In this paper, we propose a speech stream manipulation system which can help non-professional speakers to produce fluent, professional-like speech content, in turn contributing towards better listener engagement and comprehension. We propose to achieve this task by manipulating the disfluencies in human speech, like the sounds uh and um, the filler words and awkward long silences.

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Authors:
Nisha Gandhi, Tejas Naik, Roy Shilkrot
Submitted On:
12 May 2019 - 12:38pm
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[1] Nisha Gandhi, Tejas Naik, Roy Shilkrot, "ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4466. Accessed: Sep. 21, 2019.
@article{4466-19,
url = {http://sigport.org/4466},
author = {Nisha Gandhi; Tejas Naik; Roy Shilkrot },
publisher = {IEEE SigPort},
title = {ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION},
year = {2019} }
TY - EJOUR
T1 - ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION
AU - Nisha Gandhi; Tejas Naik; Roy Shilkrot
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4466
ER -
Nisha Gandhi, Tejas Naik, Roy Shilkrot. (2019). ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION. IEEE SigPort. http://sigport.org/4466
Nisha Gandhi, Tejas Naik, Roy Shilkrot, 2019. ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION. Available at: http://sigport.org/4466.
Nisha Gandhi, Tejas Naik, Roy Shilkrot. (2019). "ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION." Web.
1. Nisha Gandhi, Tejas Naik, Roy Shilkrot. ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4466

Speaker Diarisation Using 2D Self-attentive Combination of Embeddings


Speaker diarisation systems often cluster audio segments using speaker embeddings such as i-vectors and d-vectors. Since different types of embeddings are often complementary, this paper proposes a generic framework to improve performance by combining them into a single embedding, referred to as a c-vector. This combination uses a 2-dimensional (2D) self-attentive structure, which extends the standard self-attentive layer by averaging not only across time but also across different types of embeddings.

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Authors:
Guangzhi Sun, Chao Zhang, Phil Woodland
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12 May 2019 - 11:10am
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[1] Guangzhi Sun, Chao Zhang, Phil Woodland, "Speaker Diarisation Using 2D Self-attentive Combination of Embeddings", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4465. Accessed: Sep. 21, 2019.
@article{4465-19,
url = {http://sigport.org/4465},
author = {Guangzhi Sun; Chao Zhang; Phil Woodland },
publisher = {IEEE SigPort},
title = {Speaker Diarisation Using 2D Self-attentive Combination of Embeddings},
year = {2019} }
TY - EJOUR
T1 - Speaker Diarisation Using 2D Self-attentive Combination of Embeddings
AU - Guangzhi Sun; Chao Zhang; Phil Woodland
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4465
ER -
Guangzhi Sun, Chao Zhang, Phil Woodland. (2019). Speaker Diarisation Using 2D Self-attentive Combination of Embeddings. IEEE SigPort. http://sigport.org/4465
Guangzhi Sun, Chao Zhang, Phil Woodland, 2019. Speaker Diarisation Using 2D Self-attentive Combination of Embeddings. Available at: http://sigport.org/4465.
Guangzhi Sun, Chao Zhang, Phil Woodland. (2019). "Speaker Diarisation Using 2D Self-attentive Combination of Embeddings." Web.
1. Guangzhi Sun, Chao Zhang, Phil Woodland. Speaker Diarisation Using 2D Self-attentive Combination of Embeddings [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4465

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