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Audio and Acoustic Signal Processing

SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS


Motivation:
Speech signal is an important information carrier in many social applications such as WeChat and GoogleTalk;
Modern digital technologies have put the security of speech at risk.
Solution: Watermarking is a promising solution to protect the speech signals by embedding digital data into them [1, 2].
Problem:
Many existing methods cannot satisfy the requirements of watermarking, e.g., inaudibility and robustness, simultaneously;

Paper Details

Authors:
Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI
Submitted On:
14 April 2018 - 6:16am
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ICASSP2018_Poster.pdf

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[1] Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI, "SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2814. Accessed: Jan. 20, 2019.
@article{2814-18,
url = {http://sigport.org/2814},
author = {Shengbei WANG; Weitao YUAN; Jianming WANG; Masashi UNOKI },
publisher = {IEEE SigPort},
title = {SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS},
year = {2018} }
TY - EJOUR
T1 - SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS
AU - Shengbei WANG; Weitao YUAN; Jianming WANG; Masashi UNOKI
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2814
ER -
Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI. (2018). SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS. IEEE SigPort. http://sigport.org/2814
Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI, 2018. SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS. Available at: http://sigport.org/2814.
Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI. (2018). "SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS." Web.
1. Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI. SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2814

A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios

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Authors:
Tobias Weber, Anja Klein
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14 April 2018 - 3:14am
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BC_ICASSP.pdf

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[1] Tobias Weber, Anja Klein, "A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2807. Accessed: Jan. 20, 2019.
@article{2807-18,
url = {http://sigport.org/2807},
author = {Tobias Weber; Anja Klein },
publisher = {IEEE SigPort},
title = {A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios},
year = {2018} }
TY - EJOUR
T1 - A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios
AU - Tobias Weber; Anja Klein
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2807
ER -
Tobias Weber, Anja Klein. (2018). A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios. IEEE SigPort. http://sigport.org/2807
Tobias Weber, Anja Klein, 2018. A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios. Available at: http://sigport.org/2807.
Tobias Weber, Anja Klein. (2018). "A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios." Web.
1. Tobias Weber, Anja Klein. A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2807

COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL


Specific emitter identification (SEI) is gaining popularity since it can distinguish different individuals in same type of radar emitter under complex electromagnetic environment. However, classification of signals is still a challenging task when the feature has low physical representation. In this work, we propose a compressed sensing mask feature in ambiguity domain, which can significantly improve the recognition rate of civil flight radar emitters.

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Authors:
Xinliang Zhang, Yue Qi, Hongbing Ji
Submitted On:
14 April 2018 - 3:03am
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ICASSP poster.pdf

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[1] Xinliang Zhang, Yue Qi, Hongbing Ji, "COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2805. Accessed: Jan. 20, 2019.
@article{2805-18,
url = {http://sigport.org/2805},
author = {Xinliang Zhang; Yue Qi; Hongbing Ji },
publisher = {IEEE SigPort},
title = {COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL},
year = {2018} }
TY - EJOUR
T1 - COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL
AU - Xinliang Zhang; Yue Qi; Hongbing Ji
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2805
ER -
Xinliang Zhang, Yue Qi, Hongbing Ji. (2018). COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL. IEEE SigPort. http://sigport.org/2805
Xinliang Zhang, Yue Qi, Hongbing Ji, 2018. COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL. Available at: http://sigport.org/2805.
Xinliang Zhang, Yue Qi, Hongbing Ji. (2018). "COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL." Web.
1. Xinliang Zhang, Yue Qi, Hongbing Ji. COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2805

Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning

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14 April 2018 - 5:02am
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Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning.pdf

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[1] , "Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2804. Accessed: Jan. 20, 2019.
@article{2804-18,
url = {http://sigport.org/2804},
author = { },
publisher = {IEEE SigPort},
title = {Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning},
year = {2018} }
TY - EJOUR
T1 - Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2804
ER -
. (2018). Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning. IEEE SigPort. http://sigport.org/2804
, 2018. Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning. Available at: http://sigport.org/2804.
. (2018). "Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning." Web.
1. . Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2804

Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings


The detection of overlapping speech segments is of key importance in speech applications involving analysis of multi-party conversations. The detection problem is challenging because overlapping speech segments are typically captured as short speech utterances far-field microphone recordings. In this paper, we propose detection of overlap segments using a neural network architecture consisting of long-short term memory (LSTM) models. The neural network architecture learns the presence of overlap in speech by identifying the spectrotemporal structure of overlapping speech segments.

Paper Details

Authors:
Neeraj Sajjan, Shobhana Ganesh, Neeraj Sharma, Sriram Ganapathy, Neville Ryant
Submitted On:
14 April 2018 - 2:54am
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[1] Neeraj Sajjan, Shobhana Ganesh, Neeraj Sharma, Sriram Ganapathy, Neville Ryant, "Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2803. Accessed: Jan. 20, 2019.
@article{2803-18,
url = {http://sigport.org/2803},
author = {Neeraj Sajjan; Shobhana Ganesh; Neeraj Sharma; Sriram Ganapathy; Neville Ryant },
publisher = {IEEE SigPort},
title = {Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings},
year = {2018} }
TY - EJOUR
T1 - Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings
AU - Neeraj Sajjan; Shobhana Ganesh; Neeraj Sharma; Sriram Ganapathy; Neville Ryant
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2803
ER -
Neeraj Sajjan, Shobhana Ganesh, Neeraj Sharma, Sriram Ganapathy, Neville Ryant. (2018). Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings. IEEE SigPort. http://sigport.org/2803
Neeraj Sajjan, Shobhana Ganesh, Neeraj Sharma, Sriram Ganapathy, Neville Ryant, 2018. Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings. Available at: http://sigport.org/2803.
Neeraj Sajjan, Shobhana Ganesh, Neeraj Sharma, Sriram Ganapathy, Neville Ryant. (2018). "Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings." Web.
1. Neeraj Sajjan, Shobhana Ganesh, Neeraj Sharma, Sriram Ganapathy, Neville Ryant. Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2803

END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG

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Authors:
Chien-Sheng Wu, Andrea Madotto, Genta Winata, Pascale Fung
Submitted On:
14 April 2018 - 2:48am
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wu poster

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[1] Chien-Sheng Wu, Andrea Madotto, Genta Winata, Pascale Fung, "END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2801. Accessed: Jan. 20, 2019.
@article{2801-18,
url = {http://sigport.org/2801},
author = {Chien-Sheng Wu; Andrea Madotto; Genta Winata; Pascale Fung },
publisher = {IEEE SigPort},
title = {END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG},
year = {2018} }
TY - EJOUR
T1 - END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG
AU - Chien-Sheng Wu; Andrea Madotto; Genta Winata; Pascale Fung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2801
ER -
Chien-Sheng Wu, Andrea Madotto, Genta Winata, Pascale Fung. (2018). END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG. IEEE SigPort. http://sigport.org/2801
Chien-Sheng Wu, Andrea Madotto, Genta Winata, Pascale Fung, 2018. END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG. Available at: http://sigport.org/2801.
Chien-Sheng Wu, Andrea Madotto, Genta Winata, Pascale Fung. (2018). "END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG." Web.
1. Chien-Sheng Wu, Andrea Madotto, Genta Winata, Pascale Fung. END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2801

MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION

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Authors:
Minhua Wu, Sankaran Panchapagesan, Ming Sun, Jiacheng Gu, Ryan Thomas, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal
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14 April 2018 - 2:22am
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poster

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[1] Minhua Wu, Sankaran Panchapagesan, Ming Sun, Jiacheng Gu, Ryan Thomas, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal, "MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2800. Accessed: Jan. 20, 2019.
@article{2800-18,
url = {http://sigport.org/2800},
author = {Minhua Wu; Sankaran Panchapagesan; Ming Sun; Jiacheng Gu; Ryan Thomas; Shiv Naga Prasad Vitaladevuni; Bjorn Hoffmeister; Arindam Mandal },
publisher = {IEEE SigPort},
title = {MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION},
year = {2018} }
TY - EJOUR
T1 - MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION
AU - Minhua Wu; Sankaran Panchapagesan; Ming Sun; Jiacheng Gu; Ryan Thomas; Shiv Naga Prasad Vitaladevuni; Bjorn Hoffmeister; Arindam Mandal
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2800
ER -
Minhua Wu, Sankaran Panchapagesan, Ming Sun, Jiacheng Gu, Ryan Thomas, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal. (2018). MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION. IEEE SigPort. http://sigport.org/2800
Minhua Wu, Sankaran Panchapagesan, Ming Sun, Jiacheng Gu, Ryan Thomas, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal, 2018. MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION. Available at: http://sigport.org/2800.
Minhua Wu, Sankaran Panchapagesan, Ming Sun, Jiacheng Gu, Ryan Thomas, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal. (2018). "MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION." Web.
1. Minhua Wu, Sankaran Panchapagesan, Ming Sun, Jiacheng Gu, Ryan Thomas, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal. MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2800

A Supervised Air-Tissue Boundary Segmentation Technique in real-time Magnetic Resonance Imaging Video using a Novel Measure of Contrast and Dynamic Programming

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Authors:
Prasanta Kumar Ghosh
Submitted On:
14 April 2018 - 12:13am
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ICASSP_presentation_Advait_apr_14.pdf

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[1] Prasanta Kumar Ghosh, "A Supervised Air-Tissue Boundary Segmentation Technique in real-time Magnetic Resonance Imaging Video using a Novel Measure of Contrast and Dynamic Programming", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2792. Accessed: Jan. 20, 2019.
@article{2792-18,
url = {http://sigport.org/2792},
author = {Prasanta Kumar Ghosh },
publisher = {IEEE SigPort},
title = {A Supervised Air-Tissue Boundary Segmentation Technique in real-time Magnetic Resonance Imaging Video using a Novel Measure of Contrast and Dynamic Programming},
year = {2018} }
TY - EJOUR
T1 - A Supervised Air-Tissue Boundary Segmentation Technique in real-time Magnetic Resonance Imaging Video using a Novel Measure of Contrast and Dynamic Programming
AU - Prasanta Kumar Ghosh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2792
ER -
Prasanta Kumar Ghosh. (2018). A Supervised Air-Tissue Boundary Segmentation Technique in real-time Magnetic Resonance Imaging Video using a Novel Measure of Contrast and Dynamic Programming. IEEE SigPort. http://sigport.org/2792
Prasanta Kumar Ghosh, 2018. A Supervised Air-Tissue Boundary Segmentation Technique in real-time Magnetic Resonance Imaging Video using a Novel Measure of Contrast and Dynamic Programming. Available at: http://sigport.org/2792.
Prasanta Kumar Ghosh. (2018). "A Supervised Air-Tissue Boundary Segmentation Technique in real-time Magnetic Resonance Imaging Video using a Novel Measure of Contrast and Dynamic Programming." Web.
1. Prasanta Kumar Ghosh. A Supervised Air-Tissue Boundary Segmentation Technique in real-time Magnetic Resonance Imaging Video using a Novel Measure of Contrast and Dynamic Programming [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2792

Crowdsourcing Emotional Speech


We describe the methodology for the collection and annotation of a large corpus of emotional speech data through crowdsourcing. The corpus offers 187 hours of data from 2,965 subjects. Data includes non-emotional recordings from each subject as well as recordings for five emotions: angry, happy-low-arousal, happy-high-arousal, neutral,

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Authors:
Jennifer Smith, Andreas Tsiartas, Valerie Wagner, Elizabeth Shriberg, Nikoletta Bassiou
Submitted On:
13 April 2018 - 10:55pm
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ICASSP_SenSay_Poster_180409.pdf

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[1] Jennifer Smith, Andreas Tsiartas, Valerie Wagner, Elizabeth Shriberg, Nikoletta Bassiou, "Crowdsourcing Emotional Speech", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2786. Accessed: Jan. 20, 2019.
@article{2786-18,
url = {http://sigport.org/2786},
author = {Jennifer Smith; Andreas Tsiartas; Valerie Wagner; Elizabeth Shriberg; Nikoletta Bassiou },
publisher = {IEEE SigPort},
title = {Crowdsourcing Emotional Speech},
year = {2018} }
TY - EJOUR
T1 - Crowdsourcing Emotional Speech
AU - Jennifer Smith; Andreas Tsiartas; Valerie Wagner; Elizabeth Shriberg; Nikoletta Bassiou
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2786
ER -
Jennifer Smith, Andreas Tsiartas, Valerie Wagner, Elizabeth Shriberg, Nikoletta Bassiou. (2018). Crowdsourcing Emotional Speech. IEEE SigPort. http://sigport.org/2786
Jennifer Smith, Andreas Tsiartas, Valerie Wagner, Elizabeth Shriberg, Nikoletta Bassiou, 2018. Crowdsourcing Emotional Speech. Available at: http://sigport.org/2786.
Jennifer Smith, Andreas Tsiartas, Valerie Wagner, Elizabeth Shriberg, Nikoletta Bassiou. (2018). "Crowdsourcing Emotional Speech." Web.
1. Jennifer Smith, Andreas Tsiartas, Valerie Wagner, Elizabeth Shriberg, Nikoletta Bassiou. Crowdsourcing Emotional Speech [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2786

TOWARDS PREDICTING PHYSIOLOGY FROM SPEECH DURING STRESSFUL CONVERSATIONS: HEART RATE AND RESPIRATORY SINUS ARRHYTHMIA


Being affected by mental stress during conversations might have a direct or indirect effect on our speech acoustics as well as on our physiological responses. This paper presents a study on finding the relationship between these two modalities, speech acoustics and physiology, during stressful conversations between humans. Heart rate and respiratory sinus arrhythmia have been considered as physiological variables in the present study. Two datasets, one from stress induction sessions and the other one from in-lab discussions of relationship conflicts between couples, have been analyzed.

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Authors:
Arindam Jati, Paula Williams, Brian Baucom, Panayiotis Georgiou
Submitted On:
16 April 2018 - 11:15pm
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Stress_JatiWilliamsBaucomGeorgiou_final.pptx

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Stress_JatiWilliamsBaucomGeorgiou_final_AJEdits.pptx

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[1] Arindam Jati, Paula Williams, Brian Baucom, Panayiotis Georgiou, "TOWARDS PREDICTING PHYSIOLOGY FROM SPEECH DURING STRESSFUL CONVERSATIONS: HEART RATE AND RESPIRATORY SINUS ARRHYTHMIA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2775. Accessed: Jan. 20, 2019.
@article{2775-18,
url = {http://sigport.org/2775},
author = {Arindam Jati; Paula Williams; Brian Baucom; Panayiotis Georgiou },
publisher = {IEEE SigPort},
title = {TOWARDS PREDICTING PHYSIOLOGY FROM SPEECH DURING STRESSFUL CONVERSATIONS: HEART RATE AND RESPIRATORY SINUS ARRHYTHMIA},
year = {2018} }
TY - EJOUR
T1 - TOWARDS PREDICTING PHYSIOLOGY FROM SPEECH DURING STRESSFUL CONVERSATIONS: HEART RATE AND RESPIRATORY SINUS ARRHYTHMIA
AU - Arindam Jati; Paula Williams; Brian Baucom; Panayiotis Georgiou
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2775
ER -
Arindam Jati, Paula Williams, Brian Baucom, Panayiotis Georgiou. (2018). TOWARDS PREDICTING PHYSIOLOGY FROM SPEECH DURING STRESSFUL CONVERSATIONS: HEART RATE AND RESPIRATORY SINUS ARRHYTHMIA. IEEE SigPort. http://sigport.org/2775
Arindam Jati, Paula Williams, Brian Baucom, Panayiotis Georgiou, 2018. TOWARDS PREDICTING PHYSIOLOGY FROM SPEECH DURING STRESSFUL CONVERSATIONS: HEART RATE AND RESPIRATORY SINUS ARRHYTHMIA. Available at: http://sigport.org/2775.
Arindam Jati, Paula Williams, Brian Baucom, Panayiotis Georgiou. (2018). "TOWARDS PREDICTING PHYSIOLOGY FROM SPEECH DURING STRESSFUL CONVERSATIONS: HEART RATE AND RESPIRATORY SINUS ARRHYTHMIA." Web.
1. Arindam Jati, Paula Williams, Brian Baucom, Panayiotis Georgiou. TOWARDS PREDICTING PHYSIOLOGY FROM SPEECH DURING STRESSFUL CONVERSATIONS: HEART RATE AND RESPIRATORY SINUS ARRHYTHMIA [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2775

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