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Audio and Acoustic Signal Processing

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: Nov. 12, 2018.
@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
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14 April 2018 - 2:48am
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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: Nov. 12, 2018.
@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: Nov. 12, 2018.
@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
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14 April 2018 - 12:13am
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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: Nov. 12, 2018.
@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: Nov. 12, 2018.
@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
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16 April 2018 - 11:15pm
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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: Nov. 12, 2018.
@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

QUANTISATION EFFECTS IN DISTRIBUTED OPTIMISATION


In this presentation, the effects of quantisation on distributed convex optimisation algorithms are explored via the lens of monotone operator theory. Specifically, by representing transmission quantisation via an additive noise model, we demonstrate how quantisation can be viewed as an instance of an inexact Krasnoselskii-Mann scheme. In the case of two distributed solvers, the Alternating Direction Method of Multipliers and the Primal Dual Method of Multipliers, we further demonstrate how an adaptive quantisation scheme can be constructed to reduce transmission costs between nodes.

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Authors:
Richard Heusdens
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13 April 2018 - 4:27pm
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[1] Richard Heusdens, "QUANTISATION EFFECTS IN DISTRIBUTED OPTIMISATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2764. Accessed: Nov. 12, 2018.
@article{2764-18,
url = {http://sigport.org/2764},
author = {Richard Heusdens },
publisher = {IEEE SigPort},
title = {QUANTISATION EFFECTS IN DISTRIBUTED OPTIMISATION},
year = {2018} }
TY - EJOUR
T1 - QUANTISATION EFFECTS IN DISTRIBUTED OPTIMISATION
AU - Richard Heusdens
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2764
ER -
Richard Heusdens. (2018). QUANTISATION EFFECTS IN DISTRIBUTED OPTIMISATION. IEEE SigPort. http://sigport.org/2764
Richard Heusdens, 2018. QUANTISATION EFFECTS IN DISTRIBUTED OPTIMISATION. Available at: http://sigport.org/2764.
Richard Heusdens. (2018). "QUANTISATION EFFECTS IN DISTRIBUTED OPTIMISATION." Web.
1. Richard Heusdens. QUANTISATION EFFECTS IN DISTRIBUTED OPTIMISATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2764

MULTI-VIEW AUDIO-ARTICULATORY FEATURES FOR PHONETIC RECOGNITION ON RTMRI-TIMIT DATABASE


In this paper, we investigate the use of articulatory informa-
tion, and more specifically real time Magnetic Resonance
Imaging (rtMRI) data of the vocal tract, to improve speech
recognition performance. For the purpose of our experiments,
we use data from the rtMRI-TIMIT database. Firstly, Scale
Invariant Feature Transform (SIFT) features are extracted for
each video frame. Afterwards, the SIFT descriptors of each
frame are transformed to a single histogram per picture, by
using the Bag of Visual Words methodology. Since this kind

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Authors:
Ioannis Douros, Athanasios Katsamanis, Petros Maragos
Submitted On:
13 April 2018 - 2:13pm
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ICASSP_2018_poster_final.pdf

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[1] Ioannis Douros, Athanasios Katsamanis, Petros Maragos, "MULTI-VIEW AUDIO-ARTICULATORY FEATURES FOR PHONETIC RECOGNITION ON RTMRI-TIMIT DATABASE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2734. Accessed: Nov. 12, 2018.
@article{2734-18,
url = {http://sigport.org/2734},
author = {Ioannis Douros; Athanasios Katsamanis; Petros Maragos },
publisher = {IEEE SigPort},
title = {MULTI-VIEW AUDIO-ARTICULATORY FEATURES FOR PHONETIC RECOGNITION ON RTMRI-TIMIT DATABASE},
year = {2018} }
TY - EJOUR
T1 - MULTI-VIEW AUDIO-ARTICULATORY FEATURES FOR PHONETIC RECOGNITION ON RTMRI-TIMIT DATABASE
AU - Ioannis Douros; Athanasios Katsamanis; Petros Maragos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2734
ER -
Ioannis Douros, Athanasios Katsamanis, Petros Maragos. (2018). MULTI-VIEW AUDIO-ARTICULATORY FEATURES FOR PHONETIC RECOGNITION ON RTMRI-TIMIT DATABASE. IEEE SigPort. http://sigport.org/2734
Ioannis Douros, Athanasios Katsamanis, Petros Maragos, 2018. MULTI-VIEW AUDIO-ARTICULATORY FEATURES FOR PHONETIC RECOGNITION ON RTMRI-TIMIT DATABASE. Available at: http://sigport.org/2734.
Ioannis Douros, Athanasios Katsamanis, Petros Maragos. (2018). "MULTI-VIEW AUDIO-ARTICULATORY FEATURES FOR PHONETIC RECOGNITION ON RTMRI-TIMIT DATABASE." Web.
1. Ioannis Douros, Athanasios Katsamanis, Petros Maragos. MULTI-VIEW AUDIO-ARTICULATORY FEATURES FOR PHONETIC RECOGNITION ON RTMRI-TIMIT DATABASE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2734

A CONVERSATIONAL NEURAL LANGUAGE MODEL FOR SPEECH RECOGNITION IN DIGITAL ASSISTANTS


Speech recognition in digital assistants such as Google Assistant can
potentially benefit from the use of conversational context consisting of user
queries and responses from the agent. We explore the use of recurrent,
Long Short-Term Memory (LSTM), neural language models (LMs) to model the conversations
in a digital assistant. Our proposed methods effectively capture the context of
previous utterances in a conversation without modifying the underlying LSTM
architecture. We demonstrate a 4% relative improvement in recognition performance

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Authors:
Eunjoon Cho, Shankar Kumar
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13 April 2018 - 1:19pm
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[1] Eunjoon Cho, Shankar Kumar, "A CONVERSATIONAL NEURAL LANGUAGE MODEL FOR SPEECH RECOGNITION IN DIGITAL ASSISTANTS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2732. Accessed: Nov. 12, 2018.
@article{2732-18,
url = {http://sigport.org/2732},
author = {Eunjoon Cho; Shankar Kumar },
publisher = {IEEE SigPort},
title = {A CONVERSATIONAL NEURAL LANGUAGE MODEL FOR SPEECH RECOGNITION IN DIGITAL ASSISTANTS},
year = {2018} }
TY - EJOUR
T1 - A CONVERSATIONAL NEURAL LANGUAGE MODEL FOR SPEECH RECOGNITION IN DIGITAL ASSISTANTS
AU - Eunjoon Cho; Shankar Kumar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2732
ER -
Eunjoon Cho, Shankar Kumar. (2018). A CONVERSATIONAL NEURAL LANGUAGE MODEL FOR SPEECH RECOGNITION IN DIGITAL ASSISTANTS. IEEE SigPort. http://sigport.org/2732
Eunjoon Cho, Shankar Kumar, 2018. A CONVERSATIONAL NEURAL LANGUAGE MODEL FOR SPEECH RECOGNITION IN DIGITAL ASSISTANTS. Available at: http://sigport.org/2732.
Eunjoon Cho, Shankar Kumar. (2018). "A CONVERSATIONAL NEURAL LANGUAGE MODEL FOR SPEECH RECOGNITION IN DIGITAL ASSISTANTS." Web.
1. Eunjoon Cho, Shankar Kumar. A CONVERSATIONAL NEURAL LANGUAGE MODEL FOR SPEECH RECOGNITION IN DIGITAL ASSISTANTS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2732

USING ACCELEROMETRIC AND GYROSCOPIC DATA TO IMPROVE BLOOD PRESSURE PREDICTION FROM PULSE TRANSIT TIME USING RECURRENT NEURAL NETWORK

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Authors:
Shrimanti Ghosh, Ankur Banerjee, Nilanjan Ray, Peter W Wood, Pierre Boulanger, Raj Padwal
Submitted On:
13 April 2018 - 12:27pm
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[1] Shrimanti Ghosh, Ankur Banerjee, Nilanjan Ray, Peter W Wood, Pierre Boulanger, Raj Padwal, "USING ACCELEROMETRIC AND GYROSCOPIC DATA TO IMPROVE BLOOD PRESSURE PREDICTION FROM PULSE TRANSIT TIME USING RECURRENT NEURAL NETWORK", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2727. Accessed: Nov. 12, 2018.
@article{2727-18,
url = {http://sigport.org/2727},
author = {Shrimanti Ghosh; Ankur Banerjee; Nilanjan Ray; Peter W Wood; Pierre Boulanger; Raj Padwal },
publisher = {IEEE SigPort},
title = {USING ACCELEROMETRIC AND GYROSCOPIC DATA TO IMPROVE BLOOD PRESSURE PREDICTION FROM PULSE TRANSIT TIME USING RECURRENT NEURAL NETWORK},
year = {2018} }
TY - EJOUR
T1 - USING ACCELEROMETRIC AND GYROSCOPIC DATA TO IMPROVE BLOOD PRESSURE PREDICTION FROM PULSE TRANSIT TIME USING RECURRENT NEURAL NETWORK
AU - Shrimanti Ghosh; Ankur Banerjee; Nilanjan Ray; Peter W Wood; Pierre Boulanger; Raj Padwal
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2727
ER -
Shrimanti Ghosh, Ankur Banerjee, Nilanjan Ray, Peter W Wood, Pierre Boulanger, Raj Padwal. (2018). USING ACCELEROMETRIC AND GYROSCOPIC DATA TO IMPROVE BLOOD PRESSURE PREDICTION FROM PULSE TRANSIT TIME USING RECURRENT NEURAL NETWORK. IEEE SigPort. http://sigport.org/2727
Shrimanti Ghosh, Ankur Banerjee, Nilanjan Ray, Peter W Wood, Pierre Boulanger, Raj Padwal, 2018. USING ACCELEROMETRIC AND GYROSCOPIC DATA TO IMPROVE BLOOD PRESSURE PREDICTION FROM PULSE TRANSIT TIME USING RECURRENT NEURAL NETWORK. Available at: http://sigport.org/2727.
Shrimanti Ghosh, Ankur Banerjee, Nilanjan Ray, Peter W Wood, Pierre Boulanger, Raj Padwal. (2018). "USING ACCELEROMETRIC AND GYROSCOPIC DATA TO IMPROVE BLOOD PRESSURE PREDICTION FROM PULSE TRANSIT TIME USING RECURRENT NEURAL NETWORK." Web.
1. Shrimanti Ghosh, Ankur Banerjee, Nilanjan Ray, Peter W Wood, Pierre Boulanger, Raj Padwal. USING ACCELEROMETRIC AND GYROSCOPIC DATA TO IMPROVE BLOOD PRESSURE PREDICTION FROM PULSE TRANSIT TIME USING RECURRENT NEURAL NETWORK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2727

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