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ICASSP 2020

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.

Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides

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Authors:
Morgan Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdelrahman Mohamed, Emmanuel Dupoux
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6 June 2020 - 10:30pm
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Libri-Light - A Benchmark for ASR with Limited or No Supervision -- ICASSP 2020.pdf

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[1] Morgan Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdelrahman Mohamed, Emmanuel Dupoux, "Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5458. Accessed: Jun. 07, 2020.
@article{5458-20,
url = {http://sigport.org/5458},
author = {Morgan Rivière; Weiyi Zheng; Evgeny Kharitonov; Qiantong Xu; Pierre-Emmanuel Mazaré; Julien Karadayi; Vitaliy Liptchinsky; Ronan Collobert; Christian Fuegen; Tatiana Likhomanenko; Gabriel Synnaeve; Armand Joulin; Abdelrahman Mohamed; Emmanuel Dupoux },
publisher = {IEEE SigPort},
title = {Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides},
year = {2020} }
TY - EJOUR
T1 - Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides
AU - Morgan Rivière; Weiyi Zheng; Evgeny Kharitonov; Qiantong Xu; Pierre-Emmanuel Mazaré; Julien Karadayi; Vitaliy Liptchinsky; Ronan Collobert; Christian Fuegen; Tatiana Likhomanenko; Gabriel Synnaeve; Armand Joulin; Abdelrahman Mohamed; Emmanuel Dupoux
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5458
ER -
Morgan Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdelrahman Mohamed, Emmanuel Dupoux. (2020). Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides. IEEE SigPort. http://sigport.org/5458
Morgan Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdelrahman Mohamed, Emmanuel Dupoux, 2020. Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides. Available at: http://sigport.org/5458.
Morgan Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdelrahman Mohamed, Emmanuel Dupoux. (2020). "Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides." Web.
1. Morgan Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdelrahman Mohamed, Emmanuel Dupoux. Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5458

Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides

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Authors:
Ann Lee, Awni Hannun
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6 June 2020 - 10:19pm
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Self-Training for End-to-End Speech Recognition - ICASSP 2020.pdf

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[1] Ann Lee, Awni Hannun, "Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5457. Accessed: Jun. 07, 2020.
@article{5457-20,
url = {http://sigport.org/5457},
author = { Ann Lee; Awni Hannun },
publisher = {IEEE SigPort},
title = {Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides},
year = {2020} }
TY - EJOUR
T1 - Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides
AU - Ann Lee; Awni Hannun
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5457
ER -
Ann Lee, Awni Hannun. (2020). Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides. IEEE SigPort. http://sigport.org/5457
Ann Lee, Awni Hannun, 2020. Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides. Available at: http://sigport.org/5457.
Ann Lee, Awni Hannun. (2020). "Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides." Web.
1. Ann Lee, Awni Hannun. Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5457

DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS


Accurate and efficient methods for Direction of Arrival (DOA) estimation play an important role in mmWave channel estimation methods. This estimation procedure can potentially be affected by the different RF and analog components in the communication system. Such components add an unknown, nonlinear distortion to the received signal. This work looks at addressing this problem of DOA estimation for a general case of a nonlinear distortion of the received signal. Two different scenarios for angle recovery are considered here: with the use of pilot symbols and without the use of pilots.

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5 June 2020 - 9:20pm
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ICASSP_Presentation_Aditya Sant_Handout.pdf

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[1] , "DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5456. Accessed: Jun. 07, 2020.
@article{5456-20,
url = {http://sigport.org/5456},
author = { },
publisher = {IEEE SigPort},
title = {DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS},
year = {2020} }
TY - EJOUR
T1 - DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5456
ER -
. (2020). DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS. IEEE SigPort. http://sigport.org/5456
, 2020. DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS. Available at: http://sigport.org/5456.
. (2020). "DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS." Web.
1. . DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5456

Filtering out time-frequency areas using Gabor multipliers


We address the problem of filtering out localized time-frequency components in signals. The problem is formulatedas a minimization of a suitable quadratic form, that involves adata fidelity term on the short-time Fourier transform outsidethe support of the undesired component, and an energy pe-nalization term inside the support. The minimization yields alinear system whose solution can be expressed in closed formusing Gabor multipliers.

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5 June 2020 - 6:56am
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icassp_2020_Mkreme.pdf

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[1] , "Filtering out time-frequency areas using Gabor multipliers", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5455. Accessed: Jun. 07, 2020.
@article{5455-20,
url = {http://sigport.org/5455},
author = { },
publisher = {IEEE SigPort},
title = {Filtering out time-frequency areas using Gabor multipliers},
year = {2020} }
TY - EJOUR
T1 - Filtering out time-frequency areas using Gabor multipliers
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5455
ER -
. (2020). Filtering out time-frequency areas using Gabor multipliers. IEEE SigPort. http://sigport.org/5455
, 2020. Filtering out time-frequency areas using Gabor multipliers. Available at: http://sigport.org/5455.
. (2020). "Filtering out time-frequency areas using Gabor multipliers." Web.
1. . Filtering out time-frequency areas using Gabor multipliers [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5455

Differentiable Branching in Deep Networks for Fast Inference


In this paper, we consider the design of deep neural networks augmented with multiple auxiliary classifiers departing from the main (backbone) network. These classifiers can be used to perform early-exit from the network at various layers, making them convenient for energy-constrained applications such as IoT, embedded devices, or Fog computing. However, designing an optimized early-exit strategy is a difficult task, generally requiring a large amount of manual fine-tuning.

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Authors:
Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini
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5 June 2020 - 4:28am
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[1] Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini, "Differentiable Branching in Deep Networks for Fast Inference", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5453. Accessed: Jun. 07, 2020.
@article{5453-20,
url = {http://sigport.org/5453},
author = {Simone Scardapane; Danilo Comminiello; Michele Scarpiniti; Enzo Baccarelli; Aurelio Uncini },
publisher = {IEEE SigPort},
title = {Differentiable Branching in Deep Networks for Fast Inference},
year = {2020} }
TY - EJOUR
T1 - Differentiable Branching in Deep Networks for Fast Inference
AU - Simone Scardapane; Danilo Comminiello; Michele Scarpiniti; Enzo Baccarelli; Aurelio Uncini
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5453
ER -
Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini. (2020). Differentiable Branching in Deep Networks for Fast Inference. IEEE SigPort. http://sigport.org/5453
Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini, 2020. Differentiable Branching in Deep Networks for Fast Inference. Available at: http://sigport.org/5453.
Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini. (2020). "Differentiable Branching in Deep Networks for Fast Inference." Web.
1. Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini. Differentiable Branching in Deep Networks for Fast Inference [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5453

UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM


Multiple-object tracking (MOT) and classification are core technologies for processing moving point clouds in radar or lidar applications. For accurate object classification, the one-to-one association relationship between the model of each objects' motion (trackers) and the observation sequences including auxiliary features (e.g., radar cross section) is important.

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Authors:
Yuta Kawachi, Teppei Suzuki
Submitted On:
4 June 2020 - 7:59am
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ICASSP2020_A0_vert_ykawachi_submit_20200415_3.pdf

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[1] Yuta Kawachi, Teppei Suzuki, "UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5452. Accessed: Jun. 07, 2020.
@article{5452-20,
url = {http://sigport.org/5452},
author = {Yuta Kawachi; Teppei Suzuki },
publisher = {IEEE SigPort},
title = {UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM},
year = {2020} }
TY - EJOUR
T1 - UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM
AU - Yuta Kawachi; Teppei Suzuki
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5452
ER -
Yuta Kawachi, Teppei Suzuki. (2020). UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM. IEEE SigPort. http://sigport.org/5452
Yuta Kawachi, Teppei Suzuki, 2020. UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM. Available at: http://sigport.org/5452.
Yuta Kawachi, Teppei Suzuki. (2020). "UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM." Web.
1. Yuta Kawachi, Teppei Suzuki. UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5452

UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM


Multiple-object tracking (MOT) and classification are core technologies for processing moving point clouds in radar or lidar applications. For accurate object classification, the one-to-one association relationship between the model of each objects' motion (trackers) and the observation sequences including auxiliary features (e.g., radar cross section) is important.

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Authors:
Yuta Kawachi, Teppei Suzuki
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4 June 2020 - 7:59am
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[1] Yuta Kawachi, Teppei Suzuki, "UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5451. Accessed: Jun. 07, 2020.
@article{5451-20,
url = {http://sigport.org/5451},
author = {Yuta Kawachi; Teppei Suzuki },
publisher = {IEEE SigPort},
title = {UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM},
year = {2020} }
TY - EJOUR
T1 - UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM
AU - Yuta Kawachi; Teppei Suzuki
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5451
ER -
Yuta Kawachi, Teppei Suzuki. (2020). UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM. IEEE SigPort. http://sigport.org/5451
Yuta Kawachi, Teppei Suzuki, 2020. UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM. Available at: http://sigport.org/5451.
Yuta Kawachi, Teppei Suzuki. (2020). "UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM." Web.
1. Yuta Kawachi, Teppei Suzuki. UNSUPERVISED AUTO-ENCODING MULTIPLE-OBJECT TRACKER FOR CONSTRAINT-CONSISTENT COMBINATORIAL PROBLEM [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5451

ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network


Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super-resolution that is able to produce photorealistic images. Despite the visual quality of these generated images, there is still room for improvement. In this fashion, the model is extended to further improve the perceptual quality of the images. We have designed a network architecture with a novel basic block to replace the one used by the original ESRGAN. Moreover, we introduce noise inputs to the generator network in order to exploit stochastic variation.

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3 June 2020 - 8:27am
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Presentation ICASSP 2020.pdf

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[1] , "ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5450. Accessed: Jun. 07, 2020.
@article{5450-20,
url = {http://sigport.org/5450},
author = { },
publisher = {IEEE SigPort},
title = {ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network},
year = {2020} }
TY - EJOUR
T1 - ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5450
ER -
. (2020). ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network. IEEE SigPort. http://sigport.org/5450
, 2020. ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network. Available at: http://sigport.org/5450.
. (2020). "ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network." Web.
1. . ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5450

Deliberation Model Based Two-Pass End-to-End Speech Recognition

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Authors:
Tara Sainath, Ruoming Pang, Rohit Prabhavalkar
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1 June 2020 - 3:13pm
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Deliberation Model Based Two-Pass End-to-End Speech Recognition (ICASSP 2020).pdf

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[1] Tara Sainath, Ruoming Pang, Rohit Prabhavalkar, "Deliberation Model Based Two-Pass End-to-End Speech Recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5449. Accessed: Jun. 07, 2020.
@article{5449-20,
url = {http://sigport.org/5449},
author = {Tara Sainath; Ruoming Pang; Rohit Prabhavalkar },
publisher = {IEEE SigPort},
title = {Deliberation Model Based Two-Pass End-to-End Speech Recognition},
year = {2020} }
TY - EJOUR
T1 - Deliberation Model Based Two-Pass End-to-End Speech Recognition
AU - Tara Sainath; Ruoming Pang; Rohit Prabhavalkar
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5449
ER -
Tara Sainath, Ruoming Pang, Rohit Prabhavalkar. (2020). Deliberation Model Based Two-Pass End-to-End Speech Recognition. IEEE SigPort. http://sigport.org/5449
Tara Sainath, Ruoming Pang, Rohit Prabhavalkar, 2020. Deliberation Model Based Two-Pass End-to-End Speech Recognition. Available at: http://sigport.org/5449.
Tara Sainath, Ruoming Pang, Rohit Prabhavalkar. (2020). "Deliberation Model Based Two-Pass End-to-End Speech Recognition." Web.
1. Tara Sainath, Ruoming Pang, Rohit Prabhavalkar. Deliberation Model Based Two-Pass End-to-End Speech Recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5449

PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES


Phoneme boundary detection plays an essential first step for a variety of speech processing applications such as speaker diarization, speech science, keyword spotting, etc. In this work, we propose a neural architecture coupled with a parameterized structured loss function to learn segmental representations for the task of phoneme boundary detection. First, we evaluated our model when the spoken phonemes were not given as input.

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Authors:
Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi
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1 June 2020 - 11:44am
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2020-05 - ICASSP - Phoneme Boundary Detection using Learnable Segmental Features.pdf

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[1] Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi, "PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5448. Accessed: Jun. 07, 2020.
@article{5448-20,
url = {http://sigport.org/5448},
author = {Felix Kreuk; Yaniv Sheena; Joseph Keshet; Yossi Adi },
publisher = {IEEE SigPort},
title = {PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES},
year = {2020} }
TY - EJOUR
T1 - PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES
AU - Felix Kreuk; Yaniv Sheena; Joseph Keshet; Yossi Adi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5448
ER -
Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi. (2020). PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES. IEEE SigPort. http://sigport.org/5448
Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi, 2020. PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES. Available at: http://sigport.org/5448.
Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi. (2020). "PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES." Web.
1. Felix Kreuk, Yaniv Sheena, Joseph Keshet, Yossi Adi. PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5448

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