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In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system.
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“Speaker-adaptation for hybrid hmm-ann continuous speech recognition system,”
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Sinno Jialin Pan and Qiang Yang, · 2010
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“Adaptation of context-dependent deep neural networks for automatic speech recognition,”
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“Sensitive keyword spotting for crime analysis,”
H.P. Kavya and Veena Karjigi, · 2014
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“Automatic speech recognition for under-resourced languages: A survey,”
Laurent Besacier, Etienne Barnard, Alexey Karpov, and Tanja Schultz, · 2014
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“Cross-language phoneme mapping for phonetic search keyword spotting in continuous speech of under-resourced languages,”
Ella Tetariy, Yossi Bar-Yosef, Vered Silber-Varod, Michal Gishri, Ruthi Alon-Lavi, Vered Aharonson, and Ami Moyal, · 2015
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“Unsupervised machine translation using monolingual corpora only,”
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato, · 2017
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“A neural attention model for speech command recognition,”
Douglas Coimbra de Andrade, Sabato Leo, Martin Loesener Da Silva Viana, and Christoph Bernkopf, · 2018
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“Listening to the world improves speech command recognition,”
Brian McMahan and Delip Rao, · 2018
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“Efficient keyword spotting using time delay neural networks,”
Samuel Myer and Vikrant Singh Tomar, · 2018
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“Speech Commands: A dataset for limited-vocabulary speech recognition,”
Pete Warden, · 2018
Cited alongside, same era.
“Smart phone as a controlling device for smart home using speech recognition,”
Sakshi Bajpai and D. Radha, · 2019
Cited alongside, same era.
“Speech command classification system for sinhala language based on automatic speech recognition,”
Thilini Dinushika, Lakshika Kavmini, Pamoda Abeyawardhana, Uthayasanker Thayasivam, and Sanath Jayasena, · 2019
Cited alongside, same era.
Raghav Menon, Herman Kamper, Ewald van der Westhuizen, John Quinn, and Thomas Niesler, · 2019
Cited alongside, same era.
“Learning problem-agnostic speech representations from multiple self-supervised tasks,”
“wav2vec 2.0: A framework for self-supervised learning of speech representations,”
Alexei Baevski, Henry Zhou, Abdel rahman Mohamed, and Michael Auli, · 2020
Later among the works it cites.
“Unsupervised pre-training for voice activation,”
Aliaksei Kolesau and Dmitrij Šešok, · 2020
Later among the works it cites.
“Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources,”
Yun-Yun Tsai, Pin-Yu Chen, and Tsung-Yi Ho, · 2020
Later among the works it cites.
Hu Hu, Sabato Marco Siniscalchi, Yannan Wang, and Chin-Hui Lee, · 2020
Later among the works it cites.
“Database for arabic speech commands recognition,”
Lina Benamer and Osama Alkishriwo, · 2020
Later among the works it cites.
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Santiago Pascual, Mirco Ravanelli, Joan Serra, Antonio Bonafonte, and Yoshua Bengio, · 2019
Cited alongside, same era.
“wav2vec: Unsupervised pre-training for speech recognition,”
Steffen Schneider, Alexei Baevski, Ronan Collobert, and Michael Auli, · 2019
Cited alongside, same era.
“vq-wav2vec: Self-supervised learning of discrete speech representations,”
Alexei Baevski, Steffen Schneider, and Michael Auli, · 2019
Cited alongside, same era.
“Adversarial reprogramming of neural networks,”
Gamaleldin F. Elsayed, Ian Goodfellow, and Jascha Sohl-Dickstein, · 2019
Cited alongside, same era.
“SpecAugment: A simple data augmentation method for automatic speech recognition,”
Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, and Quoc V. Le, · 2019
Cited alongside, same era.
Somshubra Majumdar and Boris Ginsburg, · 2020
Cited alongside, same era.
“Training keyword spotters with limited and synthesized speech data,”
James Lin, Kevin Kilgour, Dominik Roblek, and Matthew Sharifi, · 2020
Cited alongside, same era.
“Multi-task self-supervised learning for robust speech recognition,”
Mirco Ravanelli, Jianyuan Zhong, Santiago Pascual, Pawel Swietojanski, Joao Monteiro, Jan Trmal, and Yoshua Bengio, · 2020
Cited alongside, same era.
“Wav2KWS: Transfer learning from speech representations for keyword spotting,”
Deokjin Seo, Heung-Seon Oh, and Yuchul Jung, · 2021
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“Learning efficient representations for keyword spotting with triplet loss,”
Roman Vygon and Nikolay Mikhaylovskiy, · 2021
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“Representation based meta-learning for few-shot spoken intent recognition,”
Ashish Mittal, Samarth Bharadwaj, Shreya Khare, Saneem Chemmengath, Karthik Sankaranarayanan, and Brian Kingsbury, · 2021
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“Voice2Series: Reprogramming acoustic models for time series classification,”
Chao-Han Huck Yang, Yun-Yun Tsai, and Pin-Yu Chen, · 2021
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“Voice activation for low-resource languages,”
Aliaksei Kolesau and Dmitrij Šešok, · 2021
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“A speech command control-based recognition system for dysarthric patients based on deep learning technology,”
Yu-Yi Lin, Wei-Zhong Zheng, Wei Chung Chu, Ji-Yan Han, Ying-Hsiu Hung, Guan-Min Ho, Chia-Yuan Chang, and Ying-Hui Lai, · 2021
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