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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.

Audio-based Detection of Explicit Content in Music


We present a novel automatic system for performing explicit content detection directly on the audio signal. Our modular approach uses an audio-to-character recognition model, a keyword spotting model associated with a dictionary of carefully chosen keywords, and a Random Forest classification model for the final decision. To the best of our knowledge, this is the first explicit content detection system based on audio only. We demonstrate the individual relevance of our modules on a set of sub-tasks and compare our approach to a lyrics-informed oracle and an end-to-end naive architecture.

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Authors:
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc
Submitted On:
27 May 2020 - 6:05am
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[1] Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc, "Audio-based Detection of Explicit Content in Music", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5441. Accessed: Aug. 05, 2020.
@article{5441-20,
url = {http://sigport.org/5441},
author = {Andrea Vaglio; Romain Hennequin; Manuel Moussallam; Gael Richard; Florence d’Alché-Buc },
publisher = {IEEE SigPort},
title = {Audio-based Detection of Explicit Content in Music},
year = {2020} }
TY - EJOUR
T1 - Audio-based Detection of Explicit Content in Music
AU - Andrea Vaglio; Romain Hennequin; Manuel Moussallam; Gael Richard; Florence d’Alché-Buc
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5441
ER -
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc. (2020). Audio-based Detection of Explicit Content in Music. IEEE SigPort. http://sigport.org/5441
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc, 2020. Audio-based Detection of Explicit Content in Music. Available at: http://sigport.org/5441.
Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc. (2020). "Audio-based Detection of Explicit Content in Music." Web.
1. Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc. Audio-based Detection of Explicit Content in Music [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5441

Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection


Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires further power efficiency. This can be achieved by neuromorphic hardware, which enables Spiking Neural Networks (SNNs) to perform inference at very low energy consumption. Spiking networks are characterized by their ability to process information efficiently, in a sparse cascade of binary events in time called spikes.

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Authors:
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak
Submitted On:
27 May 2020 - 8:49am
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[1] Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak, "Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5440. Accessed: Aug. 05, 2020.
@article{5440-20,
url = {http://sigport.org/5440},
author = {Flavio Martinelli; Giorgia Dellaferrera; Pablo Mainar; Milos Cernak },
publisher = {IEEE SigPort},
title = {Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection},
year = {2020} }
TY - EJOUR
T1 - Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection
AU - Flavio Martinelli; Giorgia Dellaferrera; Pablo Mainar; Milos Cernak
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5440
ER -
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak. (2020). Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection. IEEE SigPort. http://sigport.org/5440
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak, 2020. Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection. Available at: http://sigport.org/5440.
Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak. (2020). "Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection." Web.
1. Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak. Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5440

I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION

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Authors:
Miquel India, Javier Hernando
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26 May 2020 - 4:46am
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[1] Miquel India, Javier Hernando, "I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5438. Accessed: Aug. 05, 2020.
@article{5438-20,
url = {http://sigport.org/5438},
author = {Miquel India; Javier Hernando },
publisher = {IEEE SigPort},
title = {I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION},
year = {2020} }
TY - EJOUR
T1 - I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION
AU - Miquel India; Javier Hernando
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5438
ER -
Miquel India, Javier Hernando. (2020). I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION. IEEE SigPort. http://sigport.org/5438
Miquel India, Javier Hernando, 2020. I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION. Available at: http://sigport.org/5438.
Miquel India, Javier Hernando. (2020). "I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION." Web.
1. Miquel India, Javier Hernando. I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5438

VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING


In this paper, we consider 2-class and 3-class classification problems for classifying patients with Amyotropic Lateral Sclerosis (ALS), Parkinson’s Disease (PD) and Healthy Controls (HC) using a CNN-LSTM network. Classification performance is examined for three different tasks, namely, Spontaneous speech (SPON), Diadochoki-netic rate (DIDK) and Sustained Phonation (PHON). Experiments are conducted using speech data recorded from 60 ALS, 60 PD and60 HC subjects. Classification using SVM and DNN are considered baseline schemes.

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Authors:
Jhansi Mallela, Aravind Illa, Suhas B N, Sathvik Udupa, Yamini Belur, Nalini Atchayaram, Ravi Yadav, Pradeep Reddy, Dipanjan Gope, Prasanta Kumar Ghosh
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26 May 2020 - 1:39am
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[1] Jhansi Mallela, Aravind Illa, Suhas B N, Sathvik Udupa, Yamini Belur, Nalini Atchayaram, Ravi Yadav, Pradeep Reddy, Dipanjan Gope, Prasanta Kumar Ghosh, "VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5437. Accessed: Aug. 05, 2020.
@article{5437-20,
url = {http://sigport.org/5437},
author = {Jhansi Mallela; Aravind Illa; Suhas B N; Sathvik Udupa; Yamini Belur; Nalini Atchayaram; Ravi Yadav; Pradeep Reddy; Dipanjan Gope; Prasanta Kumar Ghosh },
publisher = {IEEE SigPort},
title = {VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING},
year = {2020} }
TY - EJOUR
T1 - VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING
AU - Jhansi Mallela; Aravind Illa; Suhas B N; Sathvik Udupa; Yamini Belur; Nalini Atchayaram; Ravi Yadav; Pradeep Reddy; Dipanjan Gope; Prasanta Kumar Ghosh
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5437
ER -
Jhansi Mallela, Aravind Illa, Suhas B N, Sathvik Udupa, Yamini Belur, Nalini Atchayaram, Ravi Yadav, Pradeep Reddy, Dipanjan Gope, Prasanta Kumar Ghosh. (2020). VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING. IEEE SigPort. http://sigport.org/5437
Jhansi Mallela, Aravind Illa, Suhas B N, Sathvik Udupa, Yamini Belur, Nalini Atchayaram, Ravi Yadav, Pradeep Reddy, Dipanjan Gope, Prasanta Kumar Ghosh, 2020. VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING. Available at: http://sigport.org/5437.
Jhansi Mallela, Aravind Illa, Suhas B N, Sathvik Udupa, Yamini Belur, Nalini Atchayaram, Ravi Yadav, Pradeep Reddy, Dipanjan Gope, Prasanta Kumar Ghosh. (2020). "VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING." Web.
1. Jhansi Mallela, Aravind Illa, Suhas B N, Sathvik Udupa, Yamini Belur, Nalini Atchayaram, Ravi Yadav, Pradeep Reddy, Dipanjan Gope, Prasanta Kumar Ghosh. VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5437

Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors

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Authors:
Yilin Liu, Fengyang Jiang, Mahanth Gowda
Submitted On:
25 May 2020 - 11:40pm
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[1] Yilin Liu, Fengyang Jiang, Mahanth Gowda, "Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5435. Accessed: Aug. 05, 2020.
@article{5435-20,
url = {http://sigport.org/5435},
author = {Yilin Liu; Fengyang Jiang; Mahanth Gowda },
publisher = {IEEE SigPort},
title = {Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors},
year = {2020} }
TY - EJOUR
T1 - Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors
AU - Yilin Liu; Fengyang Jiang; Mahanth Gowda
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5435
ER -
Yilin Liu, Fengyang Jiang, Mahanth Gowda. (2020). Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors. IEEE SigPort. http://sigport.org/5435
Yilin Liu, Fengyang Jiang, Mahanth Gowda, 2020. Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors. Available at: http://sigport.org/5435.
Yilin Liu, Fengyang Jiang, Mahanth Gowda. (2020). "Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors." Web.
1. Yilin Liu, Fengyang Jiang, Mahanth Gowda. Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5435

SPEECH RECOGNITION MODEL COMPRESSION


Deep Neural Network-based speech recognition systems are widely used in most speech processing applications. To achieve better model robustness and accuracy, these networks are constructed with millions of parameters, making them storage and compute-intensive. In this paper, we propose Bin & Quant (B&Q), a compression technique using which we were able to reduce the Deep Speech 2 speech recognition model size by 7 times for a negligible loss in accuracy.

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Authors:
Ahmed Tewfik, Raj Pawate
Submitted On:
25 May 2020 - 2:17pm
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[1] Ahmed Tewfik, Raj Pawate, "SPEECH RECOGNITION MODEL COMPRESSION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5434. Accessed: Aug. 05, 2020.
@article{5434-20,
url = {http://sigport.org/5434},
author = {Ahmed Tewfik; Raj Pawate },
publisher = {IEEE SigPort},
title = {SPEECH RECOGNITION MODEL COMPRESSION},
year = {2020} }
TY - EJOUR
T1 - SPEECH RECOGNITION MODEL COMPRESSION
AU - Ahmed Tewfik; Raj Pawate
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5434
ER -
Ahmed Tewfik, Raj Pawate. (2020). SPEECH RECOGNITION MODEL COMPRESSION. IEEE SigPort. http://sigport.org/5434
Ahmed Tewfik, Raj Pawate, 2020. SPEECH RECOGNITION MODEL COMPRESSION. Available at: http://sigport.org/5434.
Ahmed Tewfik, Raj Pawate. (2020). "SPEECH RECOGNITION MODEL COMPRESSION." Web.
1. Ahmed Tewfik, Raj Pawate. SPEECH RECOGNITION MODEL COMPRESSION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5434

Acoustic Matching by Embedding Impulse Responses


The goal of acoustic matching is to transform an audio recording made in one acoustic environment to sound as if it had been recorded in a different environment, based on reference audio from the target environment. This paper introduces a deep learning solution for two parts of the acoustic matching problem. First, we characterize acoustic environments by mapping audio into a low-dimensional embedding invariant to speech content and speaker identity.

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Authors:
Adam Finkelstein
Submitted On:
23 May 2020 - 11:34am
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[1] Adam Finkelstein, "Acoustic Matching by Embedding Impulse Responses", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5433. Accessed: Aug. 05, 2020.
@article{5433-20,
url = {http://sigport.org/5433},
author = {Adam Finkelstein },
publisher = {IEEE SigPort},
title = {Acoustic Matching by Embedding Impulse Responses},
year = {2020} }
TY - EJOUR
T1 - Acoustic Matching by Embedding Impulse Responses
AU - Adam Finkelstein
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5433
ER -
Adam Finkelstein. (2020). Acoustic Matching by Embedding Impulse Responses. IEEE SigPort. http://sigport.org/5433
Adam Finkelstein, 2020. Acoustic Matching by Embedding Impulse Responses. Available at: http://sigport.org/5433.
Adam Finkelstein. (2020). "Acoustic Matching by Embedding Impulse Responses." Web.
1. Adam Finkelstein. Acoustic Matching by Embedding Impulse Responses [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5433

CROSS LINGUAL TRANSFER LEARNING FOR ZERO-RESOURCE DOMAIN ADAPTATION


We propose a method for zero-resource domain adaptation of DNN acoustic models, for use in low-resource situations where the only in-language training data available may be poorly matched to the intended target domain. Our method uses a multi-lingual model in which several DNN layers are shared between languages. This architecture enables domain adaptation transforms learned for one well-resourced language to be applied to an entirely different low- resource language.

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Authors:
Alberto Abad, Peter Bell, Andrea Carmantini, Steve Renals
Submitted On:
22 May 2020 - 8:32am
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[1] Alberto Abad, Peter Bell, Andrea Carmantini, Steve Renals, "CROSS LINGUAL TRANSFER LEARNING FOR ZERO-RESOURCE DOMAIN ADAPTATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5432. Accessed: Aug. 05, 2020.
@article{5432-20,
url = {http://sigport.org/5432},
author = {Alberto Abad; Peter Bell; Andrea Carmantini; Steve Renals },
publisher = {IEEE SigPort},
title = {CROSS LINGUAL TRANSFER LEARNING FOR ZERO-RESOURCE DOMAIN ADAPTATION},
year = {2020} }
TY - EJOUR
T1 - CROSS LINGUAL TRANSFER LEARNING FOR ZERO-RESOURCE DOMAIN ADAPTATION
AU - Alberto Abad; Peter Bell; Andrea Carmantini; Steve Renals
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5432
ER -
Alberto Abad, Peter Bell, Andrea Carmantini, Steve Renals. (2020). CROSS LINGUAL TRANSFER LEARNING FOR ZERO-RESOURCE DOMAIN ADAPTATION. IEEE SigPort. http://sigport.org/5432
Alberto Abad, Peter Bell, Andrea Carmantini, Steve Renals, 2020. CROSS LINGUAL TRANSFER LEARNING FOR ZERO-RESOURCE DOMAIN ADAPTATION. Available at: http://sigport.org/5432.
Alberto Abad, Peter Bell, Andrea Carmantini, Steve Renals. (2020). "CROSS LINGUAL TRANSFER LEARNING FOR ZERO-RESOURCE DOMAIN ADAPTATION." Web.
1. Alberto Abad, Peter Bell, Andrea Carmantini, Steve Renals. CROSS LINGUAL TRANSFER LEARNING FOR ZERO-RESOURCE DOMAIN ADAPTATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5432

On the Stability of Polynomial Spectral Graph Filters

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Authors:
Henry Kenlay, Dorina Thanou, Xiaowen Dong
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22 May 2020 - 5:17am
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[1] Henry Kenlay, Dorina Thanou, Xiaowen Dong, "On the Stability of Polynomial Spectral Graph Filters", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5431. Accessed: Aug. 05, 2020.
@article{5431-20,
url = {http://sigport.org/5431},
author = {Henry Kenlay; Dorina Thanou; Xiaowen Dong },
publisher = {IEEE SigPort},
title = {On the Stability of Polynomial Spectral Graph Filters},
year = {2020} }
TY - EJOUR
T1 - On the Stability of Polynomial Spectral Graph Filters
AU - Henry Kenlay; Dorina Thanou; Xiaowen Dong
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5431
ER -
Henry Kenlay, Dorina Thanou, Xiaowen Dong. (2020). On the Stability of Polynomial Spectral Graph Filters. IEEE SigPort. http://sigport.org/5431
Henry Kenlay, Dorina Thanou, Xiaowen Dong, 2020. On the Stability of Polynomial Spectral Graph Filters. Available at: http://sigport.org/5431.
Henry Kenlay, Dorina Thanou, Xiaowen Dong. (2020). "On the Stability of Polynomial Spectral Graph Filters." Web.
1. Henry Kenlay, Dorina Thanou, Xiaowen Dong. On the Stability of Polynomial Spectral Graph Filters [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5431

MULTI-HEAD ATTENTION FOR SPEECH EMOTION RECOGNITION WITH AUXILIARY LEARNING OF GENDER RECOGNITION


The paper presents a Multi-Head Attention deep learning network for Speech Emotion Recognition (SER) using Log mel-Filter Bank Energies (LFBE) spectral features as the input. The multi-head attention along with the position embedding jointly attends to information from different representations of the same LFBE input sequence. The position embedding helps in attending to the dominant emotion features by identifying positions of the features in the sequence. In addition to Multi-Head Attention and position embedding, we apply multi-task learning with gender recognition as an auxiliary task.

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Authors:
Periyasamy Paramasivam, Promod Yenigalla
Submitted On:
21 May 2020 - 11:36pm
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[1] Periyasamy Paramasivam, Promod Yenigalla, "MULTI-HEAD ATTENTION FOR SPEECH EMOTION RECOGNITION WITH AUXILIARY LEARNING OF GENDER RECOGNITION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5430. Accessed: Aug. 05, 2020.
@article{5430-20,
url = {http://sigport.org/5430},
author = {Periyasamy Paramasivam; Promod Yenigalla },
publisher = {IEEE SigPort},
title = {MULTI-HEAD ATTENTION FOR SPEECH EMOTION RECOGNITION WITH AUXILIARY LEARNING OF GENDER RECOGNITION},
year = {2020} }
TY - EJOUR
T1 - MULTI-HEAD ATTENTION FOR SPEECH EMOTION RECOGNITION WITH AUXILIARY LEARNING OF GENDER RECOGNITION
AU - Periyasamy Paramasivam; Promod Yenigalla
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5430
ER -
Periyasamy Paramasivam, Promod Yenigalla. (2020). MULTI-HEAD ATTENTION FOR SPEECH EMOTION RECOGNITION WITH AUXILIARY LEARNING OF GENDER RECOGNITION. IEEE SigPort. http://sigport.org/5430
Periyasamy Paramasivam, Promod Yenigalla, 2020. MULTI-HEAD ATTENTION FOR SPEECH EMOTION RECOGNITION WITH AUXILIARY LEARNING OF GENDER RECOGNITION. Available at: http://sigport.org/5430.
Periyasamy Paramasivam, Promod Yenigalla. (2020). "MULTI-HEAD ATTENTION FOR SPEECH EMOTION RECOGNITION WITH AUXILIARY LEARNING OF GENDER RECOGNITION." Web.
1. Periyasamy Paramasivam, Promod Yenigalla. MULTI-HEAD ATTENTION FOR SPEECH EMOTION RECOGNITION WITH AUXILIARY LEARNING OF GENDER RECOGNITION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5430

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