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Audio and Acoustic Signal Processing

Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds


Speech emotion recognition is becoming increasingly important for many applications. In real-life communication, non-verbal sounds within an utterance also play an important role for people to recognize emotion. In current studies, only few emotion recognition systems considered nonverbal sounds, such as laughter, cries or other emotion interjection, which naturally exists in our daily conversation. In this work, both verbal and nonverbal sounds within an utterance were thus considered for emotion recognition of real-life conversations.

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Authors:
Kun-Yi Huang, Chung-Hsien Wu, Qian-Bei Hong, Ming-Hsiang Su and Yi-Hsuan Chen
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9 May 2019 - 8:26am
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[1] Kun-Yi Huang, Chung-Hsien Wu, Qian-Bei Hong, Ming-Hsiang Su and Yi-Hsuan Chen, "Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4197. Accessed: Oct. 17, 2019.
@article{4197-19,
url = {http://sigport.org/4197},
author = {Kun-Yi Huang; Chung-Hsien Wu; Qian-Bei Hong; Ming-Hsiang Su and Yi-Hsuan Chen },
publisher = {IEEE SigPort},
title = {Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds},
year = {2019} }
TY - EJOUR
T1 - Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds
AU - Kun-Yi Huang; Chung-Hsien Wu; Qian-Bei Hong; Ming-Hsiang Su and Yi-Hsuan Chen
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4197
ER -
Kun-Yi Huang, Chung-Hsien Wu, Qian-Bei Hong, Ming-Hsiang Su and Yi-Hsuan Chen. (2019). Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds. IEEE SigPort. http://sigport.org/4197
Kun-Yi Huang, Chung-Hsien Wu, Qian-Bei Hong, Ming-Hsiang Su and Yi-Hsuan Chen, 2019. Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds. Available at: http://sigport.org/4197.
Kun-Yi Huang, Chung-Hsien Wu, Qian-Bei Hong, Ming-Hsiang Su and Yi-Hsuan Chen. (2019). "Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds." Web.
1. Kun-Yi Huang, Chung-Hsien Wu, Qian-Bei Hong, Ming-Hsiang Su and Yi-Hsuan Chen. Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4197

Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection


In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty detection. By integrating uncertainty into the hidden states, this type of network is able to learn the distribution of complex sequences. Because the learned distribution can be calculated explicitly in terms of probability, we can evaluate how likely an observation is then detect low-probability events as novel.

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Authors:
Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin
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9 May 2019 - 6:26am
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[1] Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin, "Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4185. Accessed: Oct. 17, 2019.
@article{4185-19,
url = {http://sigport.org/4185},
author = {Duong Nguyen ; Oliver S. Kirsebom ; Fábio Frazão ; Ronan Fablet ; Stan Matwin },
publisher = {IEEE SigPort},
title = {Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection},
year = {2019} }
TY - EJOUR
T1 - Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
AU - Duong Nguyen ; Oliver S. Kirsebom ; Fábio Frazão ; Ronan Fablet ; Stan Matwin
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4185
ER -
Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin. (2019). Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection. IEEE SigPort. http://sigport.org/4185
Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin, 2019. Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection. Available at: http://sigport.org/4185.
Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin. (2019). "Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection." Web.
1. Duong Nguyen , Oliver S. Kirsebom , Fábio Frazão , Ronan Fablet , Stan Matwin. Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4185

Audio Caption: Listen and Tell

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9 May 2019 - 4:47am
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ICASSP POSTER.pdf

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[1] , "Audio Caption: Listen and Tell", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4171. Accessed: Oct. 17, 2019.
@article{4171-19,
url = {http://sigport.org/4171},
author = { },
publisher = {IEEE SigPort},
title = {Audio Caption: Listen and Tell},
year = {2019} }
TY - EJOUR
T1 - Audio Caption: Listen and Tell
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4171
ER -
. (2019). Audio Caption: Listen and Tell. IEEE SigPort. http://sigport.org/4171
, 2019. Audio Caption: Listen and Tell. Available at: http://sigport.org/4171.
. (2019). "Audio Caption: Listen and Tell." Web.
1. . Audio Caption: Listen and Tell [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4171

POLYPHONIC SOUND EVENT DETECTION USING CONVOLUTIONAL BIDIRECTIONAL LSTM AND SYNTHETIC DATA-BASED TRANSFER LEARNING

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9 May 2019 - 3:23am
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[1] , "POLYPHONIC SOUND EVENT DETECTION USING CONVOLUTIONAL BIDIRECTIONAL LSTM AND SYNTHETIC DATA-BASED TRANSFER LEARNING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4163. Accessed: Oct. 17, 2019.
@article{4163-19,
url = {http://sigport.org/4163},
author = { },
publisher = {IEEE SigPort},
title = {POLYPHONIC SOUND EVENT DETECTION USING CONVOLUTIONAL BIDIRECTIONAL LSTM AND SYNTHETIC DATA-BASED TRANSFER LEARNING},
year = {2019} }
TY - EJOUR
T1 - POLYPHONIC SOUND EVENT DETECTION USING CONVOLUTIONAL BIDIRECTIONAL LSTM AND SYNTHETIC DATA-BASED TRANSFER LEARNING
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4163
ER -
. (2019). POLYPHONIC SOUND EVENT DETECTION USING CONVOLUTIONAL BIDIRECTIONAL LSTM AND SYNTHETIC DATA-BASED TRANSFER LEARNING. IEEE SigPort. http://sigport.org/4163
, 2019. POLYPHONIC SOUND EVENT DETECTION USING CONVOLUTIONAL BIDIRECTIONAL LSTM AND SYNTHETIC DATA-BASED TRANSFER LEARNING. Available at: http://sigport.org/4163.
. (2019). "POLYPHONIC SOUND EVENT DETECTION USING CONVOLUTIONAL BIDIRECTIONAL LSTM AND SYNTHETIC DATA-BASED TRANSFER LEARNING." Web.
1. . POLYPHONIC SOUND EVENT DETECTION USING CONVOLUTIONAL BIDIRECTIONAL LSTM AND SYNTHETIC DATA-BASED TRANSFER LEARNING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4163

SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS



Audio-signal acquisition as part of wearable sensing adds an important dimension for applications such as understanding human behaviors. As part of a large study on work place behaviors, we collected audio data from individual hospital staff using custom wearable recorders. The audio features collected were limited to preserve privacy of the interactions in the hospital. A first step towards audio processing is to identify the foreground speech of the person wearing the audio badge.

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Authors:
Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S. Narayanan
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9 May 2019 - 12:29am
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SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS


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[1] Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S. Narayanan, "SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS
", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4146. Accessed: Oct. 17, 2019.
@article{4146-19,
url = {http://sigport.org/4146},
author = {Amrutha Nadarajan; Krishna Somandepalli; Shrikanth S. Narayanan },
publisher = {IEEE SigPort},
title = {SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS
},
year = {2019} }
TY - EJOUR
T1 - SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS

AU - Amrutha Nadarajan; Krishna Somandepalli; Shrikanth S. Narayanan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4146
ER -
Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S. Narayanan. (2019). SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS
. IEEE SigPort. http://sigport.org/4146
Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S. Narayanan, 2019. SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS
. Available at: http://sigport.org/4146.
Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S. Narayanan. (2019). "SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS
." Web.
1. Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S. Narayanan. SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS
 [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4146

EMBEDDING PHYSICAL AUGMENTATION AND WAVELET SCATTERING TRANSFORM TO GENERATIVE ADVERSARIAL NETWORKS FOR AUDIO CLASSIFICATION WITH LIMITED TRAINING RESOURCES


This paper addresses audio classification with limited training resources. We first investigate different types of data augmentation including physical modeling, wavelet scattering transform and Generative Adversarial Networks (GAN). We than propose a novel GAN which allows embedding of physical augmentation and wavelet scattering transform in processing. The experimental results on Google Speech Command show significant improvements of the proposed method when training with limited resources.

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Authors:
Tran Huy Dat
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8 May 2019 - 10:38pm
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Audio Classification, Limited Training, Augmentation, Generative Adversarial Networks

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[1] Tran Huy Dat, "EMBEDDING PHYSICAL AUGMENTATION AND WAVELET SCATTERING TRANSFORM TO GENERATIVE ADVERSARIAL NETWORKS FOR AUDIO CLASSIFICATION WITH LIMITED TRAINING RESOURCES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4142. Accessed: Oct. 17, 2019.
@article{4142-19,
url = {http://sigport.org/4142},
author = {Tran Huy Dat },
publisher = {IEEE SigPort},
title = {EMBEDDING PHYSICAL AUGMENTATION AND WAVELET SCATTERING TRANSFORM TO GENERATIVE ADVERSARIAL NETWORKS FOR AUDIO CLASSIFICATION WITH LIMITED TRAINING RESOURCES},
year = {2019} }
TY - EJOUR
T1 - EMBEDDING PHYSICAL AUGMENTATION AND WAVELET SCATTERING TRANSFORM TO GENERATIVE ADVERSARIAL NETWORKS FOR AUDIO CLASSIFICATION WITH LIMITED TRAINING RESOURCES
AU - Tran Huy Dat
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4142
ER -
Tran Huy Dat. (2019). EMBEDDING PHYSICAL AUGMENTATION AND WAVELET SCATTERING TRANSFORM TO GENERATIVE ADVERSARIAL NETWORKS FOR AUDIO CLASSIFICATION WITH LIMITED TRAINING RESOURCES. IEEE SigPort. http://sigport.org/4142
Tran Huy Dat, 2019. EMBEDDING PHYSICAL AUGMENTATION AND WAVELET SCATTERING TRANSFORM TO GENERATIVE ADVERSARIAL NETWORKS FOR AUDIO CLASSIFICATION WITH LIMITED TRAINING RESOURCES. Available at: http://sigport.org/4142.
Tran Huy Dat. (2019). "EMBEDDING PHYSICAL AUGMENTATION AND WAVELET SCATTERING TRANSFORM TO GENERATIVE ADVERSARIAL NETWORKS FOR AUDIO CLASSIFICATION WITH LIMITED TRAINING RESOURCES." Web.
1. Tran Huy Dat. EMBEDDING PHYSICAL AUGMENTATION AND WAVELET SCATTERING TRANSFORM TO GENERATIVE ADVERSARIAL NETWORKS FOR AUDIO CLASSIFICATION WITH LIMITED TRAINING RESOURCES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4142

MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION

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8 May 2019 - 10:14pm
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[1] , "MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4141. Accessed: Oct. 17, 2019.
@article{4141-19,
url = {http://sigport.org/4141},
author = { },
publisher = {IEEE SigPort},
title = {MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION},
year = {2019} }
TY - EJOUR
T1 - MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4141
ER -
. (2019). MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION. IEEE SigPort. http://sigport.org/4141
, 2019. MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION. Available at: http://sigport.org/4141.
. (2019). "MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION." Web.
1. . MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4141

Point Cloud Segmentation using Hierarchical Tree for Architectural Models.


Over the past few years, gathering massive volume of 3D data has become straightforward due to the proliferation of laser scanners and acquisition devices. Segmentation of such large data into meaningful segments, however, remains a challenge. Raw scans usually have missing data and varying density. In this work, we present a simple yet effective method to semantically decompose and reconstruct 3D models from point clouds. Using a hierarchical tree approach, we segment and reconstruct planar as well as non-planar scenes in an outdoor environment.

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Authors:
Omair Hassaan, Abeera Shamail, Zain Butt, Murtaza Taj
Submitted On:
8 May 2019 - 2:36pm
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[1] Omair Hassaan, Abeera Shamail, Zain Butt, Murtaza Taj, "Point Cloud Segmentation using Hierarchical Tree for Architectural Models.", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4129. Accessed: Oct. 17, 2019.
@article{4129-19,
url = {http://sigport.org/4129},
author = {Omair Hassaan; Abeera Shamail; Zain Butt; Murtaza Taj },
publisher = {IEEE SigPort},
title = {Point Cloud Segmentation using Hierarchical Tree for Architectural Models.},
year = {2019} }
TY - EJOUR
T1 - Point Cloud Segmentation using Hierarchical Tree for Architectural Models.
AU - Omair Hassaan; Abeera Shamail; Zain Butt; Murtaza Taj
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4129
ER -
Omair Hassaan, Abeera Shamail, Zain Butt, Murtaza Taj. (2019). Point Cloud Segmentation using Hierarchical Tree for Architectural Models.. IEEE SigPort. http://sigport.org/4129
Omair Hassaan, Abeera Shamail, Zain Butt, Murtaza Taj, 2019. Point Cloud Segmentation using Hierarchical Tree for Architectural Models.. Available at: http://sigport.org/4129.
Omair Hassaan, Abeera Shamail, Zain Butt, Murtaza Taj. (2019). "Point Cloud Segmentation using Hierarchical Tree for Architectural Models.." Web.
1. Omair Hassaan, Abeera Shamail, Zain Butt, Murtaza Taj. Point Cloud Segmentation using Hierarchical Tree for Architectural Models. [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4129

Dialogue State Tracking with Convolutional Semantic Taggers


In this paper, we present our novel approach to the 6th Dialogue State Tracking Challenge (DSTC6) track for end-to-end goal-oriented dialogue, in which the goal is to select the best system response from among a list of candidates in a restaurant booking conversation. Our model uses a convolutional neural network (CNN) for semantic tagging of each utterance in the dialogue history to update the dialogue state, and another CNN for predicting the best system action template.

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Authors:
Mandy Korpusik, Jim Glass
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8 May 2019 - 9:58am
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[1] Mandy Korpusik, Jim Glass, "Dialogue State Tracking with Convolutional Semantic Taggers", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4113. Accessed: Oct. 17, 2019.
@article{4113-19,
url = {http://sigport.org/4113},
author = {Mandy Korpusik; Jim Glass },
publisher = {IEEE SigPort},
title = {Dialogue State Tracking with Convolutional Semantic Taggers},
year = {2019} }
TY - EJOUR
T1 - Dialogue State Tracking with Convolutional Semantic Taggers
AU - Mandy Korpusik; Jim Glass
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4113
ER -
Mandy Korpusik, Jim Glass. (2019). Dialogue State Tracking with Convolutional Semantic Taggers. IEEE SigPort. http://sigport.org/4113
Mandy Korpusik, Jim Glass, 2019. Dialogue State Tracking with Convolutional Semantic Taggers. Available at: http://sigport.org/4113.
Mandy Korpusik, Jim Glass. (2019). "Dialogue State Tracking with Convolutional Semantic Taggers." Web.
1. Mandy Korpusik, Jim Glass. Dialogue State Tracking with Convolutional Semantic Taggers [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4113

INCREMENTAL TRANSFER LEARNING IN TWO-PASS INFORMATION BOTTLENECK BASED SPEAKER DIARIZATION SYSTEM FOR MEETINGS


The two-pass information bottleneck (TPIB) based speaker diarization system operates independently on different conversational recordings. TPIB system does not consider previously learned speaker discriminative information while diarizing new conversations. Hence, the real time factor (RTF) of TPIB system is high owing to the training time required for the artificial neural network (ANN).

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Authors:
Srikanth Madikeri, C Chandra Sekhar, Hema A Murthy
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8 May 2019 - 9:21am
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[1] Srikanth Madikeri, C Chandra Sekhar, Hema A Murthy, "INCREMENTAL TRANSFER LEARNING IN TWO-PASS INFORMATION BOTTLENECK BASED SPEAKER DIARIZATION SYSTEM FOR MEETINGS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4092. Accessed: Oct. 17, 2019.
@article{4092-19,
url = {http://sigport.org/4092},
author = {Srikanth Madikeri; C Chandra Sekhar; Hema A Murthy },
publisher = {IEEE SigPort},
title = {INCREMENTAL TRANSFER LEARNING IN TWO-PASS INFORMATION BOTTLENECK BASED SPEAKER DIARIZATION SYSTEM FOR MEETINGS},
year = {2019} }
TY - EJOUR
T1 - INCREMENTAL TRANSFER LEARNING IN TWO-PASS INFORMATION BOTTLENECK BASED SPEAKER DIARIZATION SYSTEM FOR MEETINGS
AU - Srikanth Madikeri; C Chandra Sekhar; Hema A Murthy
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4092
ER -
Srikanth Madikeri, C Chandra Sekhar, Hema A Murthy. (2019). INCREMENTAL TRANSFER LEARNING IN TWO-PASS INFORMATION BOTTLENECK BASED SPEAKER DIARIZATION SYSTEM FOR MEETINGS. IEEE SigPort. http://sigport.org/4092
Srikanth Madikeri, C Chandra Sekhar, Hema A Murthy, 2019. INCREMENTAL TRANSFER LEARNING IN TWO-PASS INFORMATION BOTTLENECK BASED SPEAKER DIARIZATION SYSTEM FOR MEETINGS. Available at: http://sigport.org/4092.
Srikanth Madikeri, C Chandra Sekhar, Hema A Murthy. (2019). "INCREMENTAL TRANSFER LEARNING IN TWO-PASS INFORMATION BOTTLENECK BASED SPEAKER DIARIZATION SYSTEM FOR MEETINGS." Web.
1. Srikanth Madikeri, C Chandra Sekhar, Hema A Murthy. INCREMENTAL TRANSFER LEARNING IN TWO-PASS INFORMATION BOTTLENECK BASED SPEAKER DIARIZATION SYSTEM FOR MEETINGS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4092

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