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Speech Processing

Adversarial Speaker Verification


The use of deep networks to extract embeddings for speaker recognition has proven successfully. However, such embeddings are susceptible to performance degradation due to the mismatches among the training, enrollment, and test conditions. In this work, we propose an adversarial speaker verification (ASV) scheme to learn the condition-invariant deep embedding via adversarial multi-task training. In ASV, a speaker classification network and a condition identification network are jointly optimized to minimize the speaker classification loss and simultaneously mini-maximize the condition loss.

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Authors:
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong
Submitted On:
12 May 2019 - 9:24pm
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asv_poster_v3.pptx

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[1] Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong, "Adversarial Speaker Verification", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4473. Accessed: Jun. 04, 2020.
@article{4473-19,
url = {http://sigport.org/4473},
author = {Zhong Meng; Yong Zhao; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Adversarial Speaker Verification},
year = {2019} }
TY - EJOUR
T1 - Adversarial Speaker Verification
AU - Zhong Meng; Yong Zhao; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4473
ER -
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. (2019). Adversarial Speaker Verification. IEEE SigPort. http://sigport.org/4473
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong, 2019. Adversarial Speaker Verification. Available at: http://sigport.org/4473.
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. (2019). "Adversarial Speaker Verification." Web.
1. Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. Adversarial Speaker Verification [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4473

Conditional Teacher-Student Learning


The teacher-student (T/S) learning has been shown to be effective for a variety of problems such as domain adaptation and model compression. One shortcoming of the T/S learning is that a teacher model, not always perfect, sporadically produces wrong guidance in form of posterior probabilities that misleads the student model towards a suboptimal performance.

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Authors:
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong
Submitted On:
12 May 2019 - 9:23pm
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cts_poster.pptx

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[1] Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong, "Conditional Teacher-Student Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4472. Accessed: Jun. 04, 2020.
@article{4472-19,
url = {http://sigport.org/4472},
author = {Zhong Meng; Jinyu Li; Yong Zhao; Yifan Gong },
publisher = {IEEE SigPort},
title = {Conditional Teacher-Student Learning},
year = {2019} }
TY - EJOUR
T1 - Conditional Teacher-Student Learning
AU - Zhong Meng; Jinyu Li; Yong Zhao; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4472
ER -
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. (2019). Conditional Teacher-Student Learning. IEEE SigPort. http://sigport.org/4472
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong, 2019. Conditional Teacher-Student Learning. Available at: http://sigport.org/4472.
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. (2019). "Conditional Teacher-Student Learning." Web.
1. Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. Conditional Teacher-Student Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4472

Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs


The language patterns followed by different speakers who play specific roles in conversational interactions provide valuable cues for the task of Speaker Role Recognition (SRR). Given the speech signal, existing algorithms typically try to find such patterns in the output of an Automatic Speech Recognition (ASR) system. In this work we propose an alternative way of revealing role-specific linguistic characteristics, by making use of role-specific ASR outputs, which are built by suitably rescoring the lattice produced after a first pass of ASR decoding.

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Authors:
David C. Atkins, Shrikanth Narayanan
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9 May 2019 - 3:15pm
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RoleSpecificLatticeRescoringICASSP19

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[1] David C. Atkins, Shrikanth Narayanan, "Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4232. Accessed: Jun. 04, 2020.
@article{4232-19,
url = {http://sigport.org/4232},
author = {David C. Atkins; Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs},
year = {2019} }
TY - EJOUR
T1 - Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs
AU - David C. Atkins; Shrikanth Narayanan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4232
ER -
David C. Atkins, Shrikanth Narayanan. (2019). Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs. IEEE SigPort. http://sigport.org/4232
David C. Atkins, Shrikanth Narayanan, 2019. Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs. Available at: http://sigport.org/4232.
David C. Atkins, Shrikanth Narayanan. (2019). "Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs." Web.
1. David C. Atkins, Shrikanth Narayanan. Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4232

Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function


The ability to identify speech with similar emotional content is valuable to many applications, including speech retrieval, surveillance, 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 learning 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 interesting research questions.

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Authors:
Reza Lotfian, Carlos Busso
Submitted On:
9 May 2019 - 12:32pm
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[1] Reza Lotfian, Carlos Busso, "Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4222. Accessed: Jun. 04, 2020.
@article{4222-19,
url = {http://sigport.org/4222},
author = {Reza Lotfian; Carlos Busso },
publisher = {IEEE SigPort},
title = {Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function},
year = {2019} }
TY - EJOUR
T1 - Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function
AU - Reza Lotfian; Carlos Busso
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4222
ER -
Reza Lotfian, Carlos Busso. (2019). Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function. IEEE SigPort. http://sigport.org/4222
Reza Lotfian, Carlos Busso, 2019. Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function. Available at: http://sigport.org/4222.
Reza Lotfian, Carlos Busso. (2019). "Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function." Web.
1. Reza Lotfian, Carlos Busso. Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4222

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: Jun. 04, 2020.
@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

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: Jun. 04, 2020.
@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

ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)

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Authors:
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps
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7 May 2019 - 6:58pm
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[1] Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps, "ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3965. Accessed: Jun. 04, 2020.
@article{3965-19,
url = {http://sigport.org/3965},
author = {Tharshini Gunendradasan; Saad Irtza; Eliathamby Ambikairajah; Julien Epps },
publisher = {IEEE SigPort},
title = {ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)},
year = {2019} }
TY - EJOUR
T1 - ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)
AU - Tharshini Gunendradasan; Saad Irtza; Eliathamby Ambikairajah; Julien Epps
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3965
ER -
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps. (2019). ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION). IEEE SigPort. http://sigport.org/3965
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps, 2019. ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION). Available at: http://sigport.org/3965.
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps. (2019). "ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)." Web.
1. Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps. ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION) [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3965

PhoneSpoof: A new dataset for spoofing attack detection in telephone channel


The results of spoofing detection systems proposed during ASVspoof Challenges 2015 and 2017 confirmed the perspective in detection of unforseen spoofing trials in microphone channel. However, telephone channel presents much more challenging conditions for spoofing detection, due to limited bandwidth, various coding standards and channel effects. Research on the topic has thus far only made use of program codecs and other telephone channel emulations. Such emulations does not quite match the real telephone spoofing attacks.

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Authors:
Galina Lavrentyeva, Sergey Novoselov, Marina Volkova, Yuri Matveev, Maria De Marsico
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7 May 2019 - 1:13pm
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[1] Galina Lavrentyeva, Sergey Novoselov, Marina Volkova, Yuri Matveev, Maria De Marsico, "PhoneSpoof: A new dataset for spoofing attack detection in telephone channel", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3927. Accessed: Jun. 04, 2020.
@article{3927-19,
url = {http://sigport.org/3927},
author = {Galina Lavrentyeva; Sergey Novoselov; Marina Volkova; Yuri Matveev; Maria De Marsico },
publisher = {IEEE SigPort},
title = {PhoneSpoof: A new dataset for spoofing attack detection in telephone channel},
year = {2019} }
TY - EJOUR
T1 - PhoneSpoof: A new dataset for spoofing attack detection in telephone channel
AU - Galina Lavrentyeva; Sergey Novoselov; Marina Volkova; Yuri Matveev; Maria De Marsico
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3927
ER -
Galina Lavrentyeva, Sergey Novoselov, Marina Volkova, Yuri Matveev, Maria De Marsico. (2019). PhoneSpoof: A new dataset for spoofing attack detection in telephone channel. IEEE SigPort. http://sigport.org/3927
Galina Lavrentyeva, Sergey Novoselov, Marina Volkova, Yuri Matveev, Maria De Marsico, 2019. PhoneSpoof: A new dataset for spoofing attack detection in telephone channel. Available at: http://sigport.org/3927.
Galina Lavrentyeva, Sergey Novoselov, Marina Volkova, Yuri Matveev, Maria De Marsico. (2019). "PhoneSpoof: A new dataset for spoofing attack detection in telephone channel." Web.
1. Galina Lavrentyeva, Sergey Novoselov, Marina Volkova, Yuri Matveev, Maria De Marsico. PhoneSpoof: A new dataset for spoofing attack detection in telephone channel [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3927

Comparison of speech tasks for automatic classification of patients with amyotrophic lateral sclerosis and healthy subjects


In this work, we consider the task of acoustic and articulatory feature based automatic classification of Amyotrophic Lateral Sclerosis (ALS) patients and healthy subjects using speech tasks. In particular, we compare the roles of different types of speech tasks, namely rehearsed speech, spontaneous speech and repeated words for this purpose. Simultaneous articulatory and speech data were recorded from 8 healthy controls and 8 ALS patients using AG501 for the classification experiments.

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Authors:
Deep Patel, BK Yaminiy, Meera SSy, Shivashankar Ny, Preethish-Kumar Veeramaniz, Seena Vengalilz, Kiran Polavarapuz, Saraswati Nashiz, Atchayaram Naliniz, Prasanta Kumar Ghosh
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24 April 2018 - 1:18am
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ICASSP_Final_April21.pdf

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[1] Deep Patel, BK Yaminiy, Meera SSy, Shivashankar Ny, Preethish-Kumar Veeramaniz, Seena Vengalilz, Kiran Polavarapuz, Saraswati Nashiz, Atchayaram Naliniz, Prasanta Kumar Ghosh, "Comparison of speech tasks for automatic classification of patients with amyotrophic lateral sclerosis and healthy subjects", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3158. Accessed: Jun. 04, 2020.
@article{3158-18,
url = {http://sigport.org/3158},
author = {Deep Patel; BK Yaminiy; Meera SSy; Shivashankar Ny; Preethish-Kumar Veeramaniz; Seena Vengalilz; Kiran Polavarapuz; Saraswati Nashiz; Atchayaram Naliniz; Prasanta Kumar Ghosh },
publisher = {IEEE SigPort},
title = {Comparison of speech tasks for automatic classification of patients with amyotrophic lateral sclerosis and healthy subjects},
year = {2018} }
TY - EJOUR
T1 - Comparison of speech tasks for automatic classification of patients with amyotrophic lateral sclerosis and healthy subjects
AU - Deep Patel; BK Yaminiy; Meera SSy; Shivashankar Ny; Preethish-Kumar Veeramaniz; Seena Vengalilz; Kiran Polavarapuz; Saraswati Nashiz; Atchayaram Naliniz; Prasanta Kumar Ghosh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3158
ER -
Deep Patel, BK Yaminiy, Meera SSy, Shivashankar Ny, Preethish-Kumar Veeramaniz, Seena Vengalilz, Kiran Polavarapuz, Saraswati Nashiz, Atchayaram Naliniz, Prasanta Kumar Ghosh. (2018). Comparison of speech tasks for automatic classification of patients with amyotrophic lateral sclerosis and healthy subjects. IEEE SigPort. http://sigport.org/3158
Deep Patel, BK Yaminiy, Meera SSy, Shivashankar Ny, Preethish-Kumar Veeramaniz, Seena Vengalilz, Kiran Polavarapuz, Saraswati Nashiz, Atchayaram Naliniz, Prasanta Kumar Ghosh, 2018. Comparison of speech tasks for automatic classification of patients with amyotrophic lateral sclerosis and healthy subjects. Available at: http://sigport.org/3158.
Deep Patel, BK Yaminiy, Meera SSy, Shivashankar Ny, Preethish-Kumar Veeramaniz, Seena Vengalilz, Kiran Polavarapuz, Saraswati Nashiz, Atchayaram Naliniz, Prasanta Kumar Ghosh. (2018). "Comparison of speech tasks for automatic classification of patients with amyotrophic lateral sclerosis and healthy subjects." Web.
1. Deep Patel, BK Yaminiy, Meera SSy, Shivashankar Ny, Preethish-Kumar Veeramaniz, Seena Vengalilz, Kiran Polavarapuz, Saraswati Nashiz, Atchayaram Naliniz, Prasanta Kumar Ghosh. Comparison of speech tasks for automatic classification of patients with amyotrophic lateral sclerosis and healthy subjects [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3158

PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION

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Authors:
Hajime Yano, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki
Submitted On:
20 April 2018 - 2:22am
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[1] Hajime Yano, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki, "PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3086. Accessed: Jun. 04, 2020.
@article{3086-18,
url = {http://sigport.org/3086},
author = {Hajime Yano; Toru Nakashika; Tetsuya Takiguchi; Yasuo Ariki },
publisher = {IEEE SigPort},
title = {PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION},
year = {2018} }
TY - EJOUR
T1 - PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION
AU - Hajime Yano; Toru Nakashika; Tetsuya Takiguchi; Yasuo Ariki
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3086
ER -
Hajime Yano, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki. (2018). PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION. IEEE SigPort. http://sigport.org/3086
Hajime Yano, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki, 2018. PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION. Available at: http://sigport.org/3086.
Hajime Yano, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki. (2018). "PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION." Web.
1. Hajime Yano, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki. PARALLEL-DATA-FREE DICTIONARY LEARNING FOR VOICE CONVERSION USING NON-NEGATIVE TUCKER DECOMPOSITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3086

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