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Training Automatic Speech Recognition (ASR) models under federated learning (FL) settings has attracted a lot of attention recently.
“Weighted finite-state transducers in speech recognition,”
Mehryar Mohri, Fernando Pereira, and Michael Riley, · 2002
Earlier work this paper cites.
“Deep speech: Scaling up end-to-end speech recognition,”
Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, et al., · 2014
Earlier work this paper cites.
“Towards end-to-end speech recognition with recurrent neural networks,”
Alex Graves and Navdeep Jaitly, · 2014
Earlier work this paper cites.
“Pyin: A fundamental frequency estimator using probabilistic threshold distributions,”
Matthias Mauch and Simon Dixon, · 2014
Earlier work this paper cites.
“Librispeech: An asr corpus based on public domain audio books,”
V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, · 2015
Earlier work this paper cites.
“Deep speech 2: End-to-end speech recognition in english and mandarin,”
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et al., · 2016
Earlier work this paper cites.
“Neural speech recognizer: Acoustic-to-word lstm model for large vocabulary speech recognition,”
Hagen Soltau, Hank Liao, and Hasim Sak, · 2016
Earlier work this paper cites.
“Federated learning: Strategies for improving communication efficiency,”
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon, · 2016
Earlier work this paper cites.
“End-to-end attention-based large vocabulary speech recognition,”
Dzmitry Bahdanau, Jan Chorowski, Dmitriy Serdyuk, Philemon Brakel, and Yoshua Bengio, · 2016
Earlier work this paper cites.
“Communication-efficient learning of deep networks from decentralized data,”
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas, · 2017
Earlier work this paper cites.
“Exploring neural transducers for end-to-end speech recognition,”
Eric Battenberg, Jitong Chen, Rewon Child, Adam Coates, Yashesh Gaur Yi Li, Hairong Liu, Sanjeev Satheesh, Anuroop Sriram, and Zhenyao Zhu, · 2017
Earlier work this paper cites.
“Joint ctc-attention based end-to-end speech recognition using multi-task learning,”
Suyoun Kim, Takaaki Hori, and Shinji Watanabe, · 2017
Earlier work this paper cites.
“End-to-end speech recognition and keyword search on low-resource languages,”
Andrew Rosenberg, Kartik Audhkhasi, Abhinav Sethy, Bhuvana Ramabhadran, and Michael Picheny, · 2017
Earlier work this paper cites.
“Tristounet: Triplet loss for speaker turn embedding,” 2017
Hervé Bredin, · 2017
Cited alongside, same era.
“A survey of deep learning techniques in speech recognition,”
Akshi Kumar, Sukriti Verma, and Himanshu Mangla, · 2018
Cited alongside, same era.
“Federated learning with non-iid data,”
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra, · 2018
Cited alongside, same era.
“State-of-the-art speech recognition with sequence-to-sequence models,”
Chung-Cheng Chiu, Tara N Sainath, Yonghui Wu, Rohit Prabhavalkar, Patrick Nguyen, Zhifeng Chen, Anjuli Kannan, Ron J Weiss, Kanishka Rao, Ekaterina Gonina, et al., · 2018
Cited alongside, same era.
“Federated optimization in heterogeneous networks,”
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith, · 2018
Cited alongside, same era.
“Training keyword spotting models on non-iid data with federated learning,”
Andrew Hard, Kurt Partridge, Cameron Nguyen, Niranjan Subrahmanya, Aishanee Shah, Pai Zhu, Ignacio Lopez Moreno, and Rajiv Mathews, · 2020
Later among the works it cites.
“A federated approach in training acoustic models,”
Dimitrios Dimitriadis, Kenichi Kumatani, Robert Gmyr, Yashesh Gaur, and Sefik Emre Eskimez, · 2020
Later among the works it cites.
“Improving on-device speaker verification using federated learning with privacy,”
Filip Granqvist, Matt Seigel, Rogier van Dalen, Áine Cahill, Stephen Shum, and Matthias Paulik, · 2020
Later among the works it cites.
“Flower: A friendly federated learning research framework,”
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D Lane, · 2020
Later among the works it cites.
“Federated learning: Challenges, methods, and future directions,”
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“Advances and open problems in federated learning,”
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al., · 2019
Cited alongside, same era.
“Federated learning for keyword spotting,”
David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, and Joseph Dureau, · 2019
Cited alongside, same era.
“Robust and communication-efficient federated learning from non-iid data,”
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek, · 2019
Cited alongside, same era.
“Transformers with convolutional context for asr,”
Abdelrahman Mohamed, Dmytro Okhonko, and Luke Zettlemoyer, · 2019
Cited alongside, same era.
“A comparison of transformer and lstm encoder decoder models for asr,”
Albert Zeyer, Parnia Bahar, Kazuki Irie, Ralf Schlüter, and Hermann Ney, · 2019
Cited alongside, same era.
“Pre-training on high-resource speech recognition improves low-resource speech-to-text translation,”
Sameer Bansal, Herman Kamper, Karen Livescu, Adam Lopez, and Sharon Goldwater, · 2019
Cited alongside, same era.
“Common voice: A massively-multilingual speech corpus,”
Rosana Ardila, Megan Branson, Kelly Davis, Michael Henretty, Michael Kohler, Josh Meyer, Reuben Morais, Lindsay Saunders, Francis M Tyers, and Gregor Weber, · 2019
Cited alongside, same era.
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith, · 2020
Later among the works it cites.
“An Evaluation of Entropy Measures for Microphone Identification,”
Gianmarco Baldini and Irene Amerini, · 2020
Later among the works it cites.
“Adaptive federated optimization,”
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan, · 2020
Later among the works it cites.
“Federated acoustic modeling for automatic speech recognition,”
Xiaodong Cui, Songtao Lu, and Brian Kingsbury, · 2021
Closest in time.
“Training speech recognition models with federated learning: A quality/cost framework,”
Dhruv Guliani, Françoise Beaufays, and Giovanni Motta, · 2021
Closest in time.
“SpeechBrain: A general-purpose speech toolkit,” 2021,
Mirco Ravanelli, Titouan Parcollet, Peter Plantinga, Aku Rouhe, Samuele Cornell, Loren Lugosch, Cem Subakan, Nauman Dawalatabad, Abdelwahab Heba, Jianyuan Zhong, Ju-Chieh Chou, Sung-Lin Yeh, Szu-Wei Fu, Chien-Feng Liao, Elena Rastorgueva, François Grondin, William Aris, Hwidong Na, Yan Gao, Renato De Mori, and Yoshua Bengio, · 2021
Closest in time.
“Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout,” 2021
Samuel Horvath, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos I. Venieris, and Nicholas D. Lane, · 2021
Closest in time.
“A first look into the carbon footprint of federated learning,” 2021
Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques, Pedro Porto Buarque de Gusmao, Daniel J. Beutel, Taner Topal, Akhil Mathur, and Nicholas D. Lane, · 2021
Closest in time.