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Speech Analysis (SPE-ANLS)

Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments

Zhaocheng Huang

Depression detection from speech continues to attract significant research attention but remains a major challenge, particularly when the speech is acquired from diverse smartphones in natural environments. Analysis methods based on vocal tract coordination have shown great promise in depression and cognitive impairment detection for quantifying relationships between features over time through eigenvalues of multi-scale cross-correlations.

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Authors:
Zhaocheng Huang
Zhaocheng Huang, Julien Epps, Dale Joachim
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28 May 2020 - 10:57pm
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[1] Zhaocheng Huang, Julien Epps, Dale Joachim, "Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5446. Accessed: Jul. 04, 2020.
@article{5446-20,
url = {http://sigport.org/5446},
author = {Zhaocheng Huang; Julien Epps; Dale Joachim },
publisher = {IEEE SigPort},
title = {Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments},
year = {2020} }
TY - EJOUR
T1 - Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments
AU - Zhaocheng Huang; Julien Epps; Dale Joachim
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5446
ER -
Zhaocheng Huang, Julien Epps, Dale Joachim. (2020). Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments. IEEE SigPort. http://sigport.org/5446
Zhaocheng Huang, Julien Epps, Dale Joachim, 2020. Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments. Available at: http://sigport.org/5446.
Zhaocheng Huang, Julien Epps, Dale Joachim. (2020). "Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments." Web.
1. Zhaocheng Huang, Julien Epps, Dale Joachim. Exploiting Vocal Tract Coordination Using Dilated CNNs for Depression Detection in Naturalistic Environments [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5446

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

Jhansi Mallela
ARAVIND ILLA

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
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: Jul. 04, 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

Ensemble feature selection for domain adaptation in speech emotion recognition

Mohammed Abdelwahab

When emotion recognition systems are used in new domains, the classification performance usually drops due to mismatches between training and testing conditions. Annotations of new data in the new domain is expensive and time demanding. Therefore, it is important to design strategies that efficiently use limited amount of new data to improve the robustness of the classification system. The use of ensembles is an attractive solution, since they can be built to perform well across different mismatches. The key challenge is to create ensembles that are diverse.

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Authors:
Mohammed Abdelwahab
Mohammed Abdelwahab, Carlos Busso
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20 May 2020 - 10:34am
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[1] Mohammed Abdelwahab, Carlos Busso, "Ensemble feature selection for domain adaptation in speech emotion recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5415. Accessed: Jul. 04, 2020.
@article{5415-20,
url = {http://sigport.org/5415},
author = {Mohammed Abdelwahab; Carlos Busso },
publisher = {IEEE SigPort},
title = {Ensemble feature selection for domain adaptation in speech emotion recognition},
year = {2020} }
TY - EJOUR
T1 - Ensemble feature selection for domain adaptation in speech emotion recognition
AU - Mohammed Abdelwahab; Carlos Busso
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5415
ER -
Mohammed Abdelwahab, Carlos Busso. (2020). Ensemble feature selection for domain adaptation in speech emotion recognition. IEEE SigPort. http://sigport.org/5415
Mohammed Abdelwahab, Carlos Busso, 2020. Ensemble feature selection for domain adaptation in speech emotion recognition. Available at: http://sigport.org/5415.
Mohammed Abdelwahab, Carlos Busso. (2020). "Ensemble feature selection for domain adaptation in speech emotion recognition." Web.
1. Mohammed Abdelwahab, Carlos Busso. Ensemble feature selection for domain adaptation in speech emotion recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5415

Incremental adaptation using active learning for acoustic emotion recognition

Mohammed Abdelwahab
Carlos Busso

The performance of speech emotion classifiers greatly degrade when the training conditions do not match the testing conditions. This problem is observed in cross-corpora evaluations, even when the corpora are similar. The lack of generalization is particularly problematic when the emotion classifiers are used in real applications. This study addresses this problem by combining active learning (AL) and supervised domain adaptation (DA) using an elegant approach for support vector machine (SVM).

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Authors:
Mohammed Abdelwahab
Carlos Busso
Mohammed Abdelwahab, Carlos Busso
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20 May 2020 - 10:26am
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[1] Mohammed Abdelwahab, Carlos Busso, "Incremental adaptation using active learning for acoustic emotion recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5413. Accessed: Jul. 04, 2020.
@article{5413-20,
url = {http://sigport.org/5413},
author = {Mohammed Abdelwahab; Carlos Busso },
publisher = {IEEE SigPort},
title = {Incremental adaptation using active learning for acoustic emotion recognition},
year = {2020} }
TY - EJOUR
T1 - Incremental adaptation using active learning for acoustic emotion recognition
AU - Mohammed Abdelwahab; Carlos Busso
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5413
ER -
Mohammed Abdelwahab, Carlos Busso. (2020). Incremental adaptation using active learning for acoustic emotion recognition. IEEE SigPort. http://sigport.org/5413
Mohammed Abdelwahab, Carlos Busso, 2020. Incremental adaptation using active learning for acoustic emotion recognition. Available at: http://sigport.org/5413.
Mohammed Abdelwahab, Carlos Busso. (2020). "Incremental adaptation using active learning for acoustic emotion recognition." Web.
1. Mohammed Abdelwahab, Carlos Busso. Incremental adaptation using active learning for acoustic emotion recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5413

Study of dense network approaches for speech emotion recognition

Mohammed Abdelwahab
Carlos Busso

Deep neural networks have been proven to be very effective in various classification problems and show great promise for emotion recognition from speech. Studies have proposed various architectures that further improve the performance of emotion recognition systems. However, there are still various open questions regarding the best approach to building a speech emotion recognition system. Would the system’s performance improve if we have more labeled data? How much do we benefit from data augmentation? What activation and regularization schemes are more beneficial?

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Authors:
Mohammed Abdelwahab
Carlos Busso
Mohammed Abdelwahab, Carlos Busso
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20 May 2020 - 9:56am
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[1] Mohammed Abdelwahab, Carlos Busso, "Study of dense network approaches for speech emotion recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5411. Accessed: Jul. 04, 2020.
@article{5411-20,
url = {http://sigport.org/5411},
author = {Mohammed Abdelwahab; Carlos Busso },
publisher = {IEEE SigPort},
title = {Study of dense network approaches for speech emotion recognition},
year = {2020} }
TY - EJOUR
T1 - Study of dense network approaches for speech emotion recognition
AU - Mohammed Abdelwahab; Carlos Busso
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5411
ER -
Mohammed Abdelwahab, Carlos Busso. (2020). Study of dense network approaches for speech emotion recognition. IEEE SigPort. http://sigport.org/5411
Mohammed Abdelwahab, Carlos Busso, 2020. Study of dense network approaches for speech emotion recognition. Available at: http://sigport.org/5411.
Mohammed Abdelwahab, Carlos Busso. (2020). "Study of dense network approaches for speech emotion recognition." Web.
1. Mohammed Abdelwahab, Carlos Busso. Study of dense network approaches for speech emotion recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5411

Retrieving speech samples with similar emotional content using a triplet loss function

John Harvill
Mohammed Abdelwahab
Carlos Busso

The ability to identify speech with similar emotional content is valuable to many applications, including speech retrieval, surveil- lance, and emotional speech synthesis. While current formulations in speech emotion recognition based on classification or regression are not appropriate for this task, solutions based on preference learn- ing offer appealing approaches for this task. This paper aims to find speech samples that are emotionally similar to an anchor speech sample provided as a query. This novel formulation opens interest- ing research questions.

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Authors:
John Harvill
Mohammed Abdelwahab
Carlos Busso
John Harvill, Mohammed AbdelWahab, Reza Lotfian, Carlos Busso
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20 May 2020 - 9:50am
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[1] John Harvill, Mohammed AbdelWahab, Reza Lotfian, Carlos Busso, "Retrieving speech samples with similar emotional content using a triplet loss function", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5409. Accessed: Jul. 04, 2020.
@article{5409-20,
url = {http://sigport.org/5409},
author = {John Harvill; Mohammed AbdelWahab; Reza Lotfian; Carlos Busso },
publisher = {IEEE SigPort},
title = {Retrieving speech samples with similar emotional content using a triplet loss function},
year = {2020} }
TY - EJOUR
T1 - Retrieving speech samples with similar emotional content using a triplet loss function
AU - John Harvill; Mohammed AbdelWahab; Reza Lotfian; Carlos Busso
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5409
ER -
John Harvill, Mohammed AbdelWahab, Reza Lotfian, Carlos Busso. (2020). Retrieving speech samples with similar emotional content using a triplet loss function. IEEE SigPort. http://sigport.org/5409
John Harvill, Mohammed AbdelWahab, Reza Lotfian, Carlos Busso, 2020. Retrieving speech samples with similar emotional content using a triplet loss function. Available at: http://sigport.org/5409.
John Harvill, Mohammed AbdelWahab, Reza Lotfian, Carlos Busso. (2020). "Retrieving speech samples with similar emotional content using a triplet loss function." Web.
1. John Harvill, Mohammed AbdelWahab, Reza Lotfian, Carlos Busso. Retrieving speech samples with similar emotional content using a triplet loss function [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5409

Curriculum learning for speech emotion recognition from crowdsourced labels

Carlos Busso

This study introduces a method to design a curriculum for machine-learning to maximize the efficiency during the training process of deep neural networks (DNNs) for speech emotion recognition. Previous studies in other machine-learning problems have shown the benefits of training a classifier following a curriculum where samples are gradually presented in increasing level of difficulty. For speech emotion recognition, the challenge is to establish a natural order of difficulty in the training set to create the curriculum.

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Authors:
Carlos Busso
Reza Lotfian, Carlos Busso
Submitted On:
20 May 2020 - 9:43am
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[1] Reza Lotfian, Carlos Busso, "Curriculum learning for speech emotion recognition from crowdsourced labels", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5408. Accessed: Jul. 04, 2020.
@article{5408-20,
url = {http://sigport.org/5408},
author = {Reza Lotfian; Carlos Busso },
publisher = {IEEE SigPort},
title = {Curriculum learning for speech emotion recognition from crowdsourced labels},
year = {2020} }
TY - EJOUR
T1 - Curriculum learning for speech emotion recognition from crowdsourced labels
AU - Reza Lotfian; Carlos Busso
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5408
ER -
Reza Lotfian, Carlos Busso. (2020). Curriculum learning for speech emotion recognition from crowdsourced labels. IEEE SigPort. http://sigport.org/5408
Reza Lotfian, Carlos Busso, 2020. Curriculum learning for speech emotion recognition from crowdsourced labels. Available at: http://sigport.org/5408.
Reza Lotfian, Carlos Busso. (2020). "Curriculum learning for speech emotion recognition from crowdsourced labels." Web.
1. Reza Lotfian, Carlos Busso. Curriculum learning for speech emotion recognition from crowdsourced labels [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5408

Modeling uncertainty in predicting emotional attributes from spontaneous speech

Carlos Busso

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Authors:
Carlos Busso
Kusha Sridhar, Carlos Busso
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20 May 2020 - 9:46am
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[1] Kusha Sridhar, Carlos Busso, "Modeling uncertainty in predicting emotional attributes from spontaneous speech", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5299. Accessed: Jul. 04, 2020.
@article{5299-20,
url = {http://sigport.org/5299},
author = {Kusha Sridhar; Carlos Busso },
publisher = {IEEE SigPort},
title = {Modeling uncertainty in predicting emotional attributes from spontaneous speech},
year = {2020} }
TY - EJOUR
T1 - Modeling uncertainty in predicting emotional attributes from spontaneous speech
AU - Kusha Sridhar; Carlos Busso
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5299
ER -
Kusha Sridhar, Carlos Busso. (2020). Modeling uncertainty in predicting emotional attributes from spontaneous speech. IEEE SigPort. http://sigport.org/5299
Kusha Sridhar, Carlos Busso, 2020. Modeling uncertainty in predicting emotional attributes from spontaneous speech. Available at: http://sigport.org/5299.
Kusha Sridhar, Carlos Busso. (2020). "Modeling uncertainty in predicting emotional attributes from spontaneous speech." Web.
1. Kusha Sridhar, Carlos Busso. Modeling uncertainty in predicting emotional attributes from spontaneous speech [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5299

ANALYSIS OF ACOUSTIC FEATURES FOR SPEECH SOUND BASED CLASSIFICATION OF ASTHMATIC AND HEALTHY SUBJECTS

shivani yadav

Non-speech sounds (cough, wheeze) are typically known to perform better than speech sounds for asthmatic and healthy subject
classification. In this work, we use sustained phonations of speech sounds, namely, /A:/, /i:/, /u:/, /eI/, /oU/, /s/, and /z/ from 47 asthmatic and 48 healthy controls. We consider INTERSPEECH 2013 Computational Paralinguistics Challenge baseline (ISCB)

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Authors:
shivani yadav
Merugu Keerthana, Dipanjan Gope, Uma Maheswari K., Prasanta Kumar Ghosh
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14 May 2020 - 1:43am
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[1] Merugu Keerthana, Dipanjan Gope, Uma Maheswari K., Prasanta Kumar Ghosh, "ANALYSIS OF ACOUSTIC FEATURES FOR SPEECH SOUND BASED CLASSIFICATION OF ASTHMATIC AND HEALTHY SUBJECTS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5225. Accessed: Jul. 04, 2020.
@article{5225-20,
url = {http://sigport.org/5225},
author = {Merugu Keerthana; Dipanjan Gope; Uma Maheswari K.; Prasanta Kumar Ghosh },
publisher = {IEEE SigPort},
title = {ANALYSIS OF ACOUSTIC FEATURES FOR SPEECH SOUND BASED CLASSIFICATION OF ASTHMATIC AND HEALTHY SUBJECTS},
year = {2020} }
TY - EJOUR
T1 - ANALYSIS OF ACOUSTIC FEATURES FOR SPEECH SOUND BASED CLASSIFICATION OF ASTHMATIC AND HEALTHY SUBJECTS
AU - Merugu Keerthana; Dipanjan Gope; Uma Maheswari K.; Prasanta Kumar Ghosh
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5225
ER -
Merugu Keerthana, Dipanjan Gope, Uma Maheswari K., Prasanta Kumar Ghosh. (2020). ANALYSIS OF ACOUSTIC FEATURES FOR SPEECH SOUND BASED CLASSIFICATION OF ASTHMATIC AND HEALTHY SUBJECTS. IEEE SigPort. http://sigport.org/5225
Merugu Keerthana, Dipanjan Gope, Uma Maheswari K., Prasanta Kumar Ghosh, 2020. ANALYSIS OF ACOUSTIC FEATURES FOR SPEECH SOUND BASED CLASSIFICATION OF ASTHMATIC AND HEALTHY SUBJECTS. Available at: http://sigport.org/5225.
Merugu Keerthana, Dipanjan Gope, Uma Maheswari K., Prasanta Kumar Ghosh. (2020). "ANALYSIS OF ACOUSTIC FEATURES FOR SPEECH SOUND BASED CLASSIFICATION OF ASTHMATIC AND HEALTHY SUBJECTS." Web.
1. Merugu Keerthana, Dipanjan Gope, Uma Maheswari K., Prasanta Kumar Ghosh. ANALYSIS OF ACOUSTIC FEATURES FOR SPEECH SOUND BASED CLASSIFICATION OF ASTHMATIC AND HEALTHY SUBJECTS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5225

A Comparison of Boosted Deep Neural Networks for Voice Activity Detection

Donald Williamson

Voice activity detection (VAD) is an integral part of speech processing for real world problems, and a lot of work has been done to improve VAD performance. Of late, deep neural networks have been used to detect the presence of speech and this has offered tremendous gains. Unfortunately, these efforts have been either restricted to feed-forward neural networks that do not adequately capture frequency and temporal correlations, or the recurrent architectures have not been adequately tested in noisy environments.

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Authors:
Donald Williamson
Harshit Krishnakumar, Donald S. Williamson
Submitted On:
12 November 2019 - 10:09pm
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[1] Harshit Krishnakumar, Donald S. Williamson, "A Comparison of Boosted Deep Neural Networks for Voice Activity Detection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4952. Accessed: Jul. 04, 2020.
@article{4952-19,
url = {http://sigport.org/4952},
author = {Harshit Krishnakumar; Donald S. Williamson },
publisher = {IEEE SigPort},
title = {A Comparison of Boosted Deep Neural Networks for Voice Activity Detection},
year = {2019} }
TY - EJOUR
T1 - A Comparison of Boosted Deep Neural Networks for Voice Activity Detection
AU - Harshit Krishnakumar; Donald S. Williamson
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4952
ER -
Harshit Krishnakumar, Donald S. Williamson. (2019). A Comparison of Boosted Deep Neural Networks for Voice Activity Detection. IEEE SigPort. http://sigport.org/4952
Harshit Krishnakumar, Donald S. Williamson, 2019. A Comparison of Boosted Deep Neural Networks for Voice Activity Detection. Available at: http://sigport.org/4952.
Harshit Krishnakumar, Donald S. Williamson. (2019). "A Comparison of Boosted Deep Neural Networks for Voice Activity Detection." Web.
1. Harshit Krishnakumar, Donald S. Williamson. A Comparison of Boosted Deep Neural Networks for Voice Activity Detection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4952

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