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Self-supervised Speech Models (S3Ms) have been proven successful in many speech downstream tasks, like ASR.
“Iemocap: interactive emotional dyadic motion capture database,”
Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Ebrahim (Abe) Kazemzadeh, Emily Mower Provost, Samuel Kim, Jeannette N. Chang, Sungbok Lee, and Shrikanth S. Narayanan, · 2008
Earlier work this paper cites.
“Librispeech: An asr corpus based on public domain audio book,”
V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, · 2015
Earlier work this paper cites.
“Deep contextualized word representations,”
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer, · 2018
Earlier work this paper cites.
“Speech commands: A dataset for limited-vocabulary speech recognition,” 2018
Pete Warden, · 2018
Earlier work this paper cites.
“Insights on representational similarity in neural networks with canonical correlation,”
Ari Morcos, Maithra Raghu, and Samy Bengio, · 2018
Earlier work this paper cites.
“BERT: Pre-training of deep bidirectional transformers for language understanding,”
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, · 2019
Earlier work this paper cites.
“Speech model pre-training for end-to-end spoken language understanding,” 2019
Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio, · 2019
Earlier work this paper cites.
“Investigating Self-Supervised Pre-Training for End-to-End Speech Translation,”
Ha Nguyen, Fethi Bougares, N. Tomashenko, Yannick Estève, and Laurent Besacier, · 2020
Earlier work this paper cites.
“Self-supervised pre-training reduces label permutation instability of speech separation,”
Sung-Feng Huang, Shun-Po Chuang, Da-Rong Liu, Yi-Chen Chen, Gene-Ping Yang, and Hung yi Lee, · 2020
Earlier work this paper cites.
“Understanding self-attention of self-supervised audio transformers,” 2020
Shu wen Yang, Andy T. Liu, and Hung yi Lee, · 2020
Earlier work this paper cites.
“Generative pre-training for speech with autoregressive predictive coding,”
Yu-An Chung and James Glass, · 2020
Earlier work this paper cites.
“Deep contextualized acoustic representations for semi-supervised speech recognition,”
S. Ling, Y. Liu, J. Salazar, and K. Kirchhoff, · 2020
Cited alongside, same era.
“Generative pre-training for speech with autoregressive predictive coding,”
Y. Chung and J. Glass, · 2020
Cited alongside, same era.
“Vector-quantized autoregressive predictive coding,”
Yu-An Chung, Hao Tang, and James Glass, · 2020
Cited alongside, same era.
“Improved speech representations with multi-target autoregressive predictive coding,”
Yu-An Chung and James Glass, · 2020
Cited alongside, same era.
“Tera: Self-supervised learning of transformer encoder representation for speech,” 2020
Andy T. Liu, Shang-Wen Li, and Hung yi Lee, · 2020
Cited alongside, same era.
“Mockingjay: Unsupervised speech representation learning with deep bidirectional transformer encoders,”
“Voxceleb: Large-scale speaker verification in the wild,”
Arsha Nagrani, Joon Son Chung, Weidi Xie, and Andrew Zisserman, · 2020
Later among the works it cites.
“Hubert: How much can a bad teacher benefit asr pre-training?,”
Wei-Ning Hsu, Yao-Hung Hubert Tsai, Benjamin Bolte, Ruslan Salakhutdinov, and Abdelrahman Mohamed, · 2021
Closest in time.
“Tabnet: Attentive interpretable tabular learning,”
Sercan Ö. Arik and Tomas Pfister, · 2021
Closest in time.
“Similarity analysis of self-supervised speech representations,”
Yu-An Chung, Yonatan Belinkov, and James Glass, · 2021
Closest in time.
“SUPERB: Speech Processing Universal PERformance Benchmark,”
Shu wen Yang, Po-Han Chi, Yung-Sung Chuang, Cheng-I Jeff Lai, Kushal Lakhotia, Yist Y. Lin, Andy T. Liu, Jiatong Shi, Xuankai Chang, Guan-Ting Lin, Tzu-Hsien Huang, Wei-Cheng Tseng, Ko tik Lee, Da-Rong Liu, Zili Huang, Shuyan Dong, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, and Hung yi Lee, · 2021
Closest in time.
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Andy T. Liu, Shu-wen Yang, Po-Han Chi, Po-chun Hsu, and Hung-yi Lee, · 2020
Cited alongside, same era.
“Improving Transformer-Based Speech Recognition with Unsupervised Pre-Training and Multi-Task Semantic Knowledge Learning,”
Song Li, Lin Li, Qingyang Hong, and Lingling Liu, · 2020
Cited alongside, same era.
“Masked pre-trained encoder base on joint ctc-transformer,” 2020
Lu Liu and Yiheng Huang, · 2020
Cited alongside, same era.
“Artie bias corpus: An open dataset for detecting demographic bias in speech applications,”
Josh Meyer, Lindy Rauchenstein, Joshua D. Eisenberg, and Nicholas Howell, · 2020
Cited alongside, same era.
“Gender in danger? evaluating speech translation technology on the must-she corpus,” 2020
Luisa Bentivogli, Beatrice Savoldi, Matteo Negri, Mattia Antonino Di Gangi, Roldano Cattoni, and Marco Turchi, · 2020
Cited alongside, same era.
“Breeding gender-aware direct speech translation systems,” 2020
Marco Gaido, Beatrice Savoldi, Luisa Bentivogli, Matteo Negri, and Marco Turchi, · 2020
Cited alongside, same era.
Dongwei Jiang, Wubo Li, Ruixiong Zhang, Miao Cao, Ne Luo, Yang Han, Wei Zou, Kun Han, and Xiangang Li, · 2021
Closest in time.
“Spoken corpora data, automatic speech recognition, and bias against african american language: The case of habitual ’be’,”
Joshua L Martin, · 2021
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“Fair Voice Biometrics: Impact of Demographic Imbalance on Group Fairness in Speaker Recognition,”
Gianni Fenu, Mirko Marras, Giacomo Medda, and Giacomo Meloni, · 2021
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“Improving fairness in speaker recognition,” 2021
Gianni Fenu, Giacomo Medda, Mirko Marras, and Giacomo Meloni, · 2021
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“How to split: the effect of word segmentation on gender bias in speech translation,” 2021
Marco Gaido, Beatrice Savoldi, Luisa Bentivogli, Matteo Negri, and Marco Turchi, · 2021
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“Robust wav2vec 2.0: Analyzing domain shift in self-supervised pre-training,” 2021
Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, and Michael Auli, · 2021
Closest in time.