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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 11 of 11 1 Self-supervised Learning with Random-projection Quantizer for Speech Recognition ... Chiu, Chung-Cheng; Qin, James; Zhang, Yu. - : arXiv, 2022 BASE Show details 2 Unsupervised Data Selection via Discrete Speech Representation for ASR ... Lu, Zhiyun; Wang, Yongqiang; Zhang, Yu. - : arXiv, 2022 BASE Show details 3 MAESTRO: Matched Speech Text Representations through Modality Matching ... Chen, Zhehuai; Zhang, Yu; Rosenberg, Andrew. - : arXiv, 2022 BASE Show details 4 Joint Unsupervised and Supervised Training for Multilingual ASR ... Bai, Junwen; Li, Bo; Zhang, Yu; Bapna, Ankur; Siddhartha, Nikhil; Sim, Khe Chai; Sainath, Tara N.. - : arXiv, 2021 Abstract: Self-supervised training has shown promising gains in pretraining models and facilitating the downstream finetuning for speech recognition, like multilingual ASR. Most existing methods adopt a 2-stage scheme where the self-supervised loss is optimized in the first pretraining stage, and the standard supervised finetuning resumes in the second stage. In this paper, we propose an end-to-end (E2E) Joint Unsupervised and Supervised Training (JUST) method to combine the supervised RNN-T loss and the self-supervised contrastive and masked language modeling (MLM) losses. We validate its performance on the public dataset Multilingual LibriSpeech (MLS), which includes 8 languages and is extremely imbalanced. On MLS, we explore (1) JUST trained from scratch, and (2) JUST finetuned from a pretrained checkpoint. Experiments show that JUST can consistently outperform other existing state-of-the-art methods, and beat the monolingual baseline by a significant margin, demonstrating JUST's capability of handling low-resource ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD URL: https://arxiv.org/abs/2111.08137 https://dx.doi.org/10.48550/arxiv.2111.08137 BASE Hide details 5 Scaling End-to-End Models for Large-Scale Multilingual ASR ... Li, Bo; Pang, Ruoming; Sainath, Tara N.. - : arXiv, 2021 BASE Show details 6 Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition ... Li, Qiujia; Zhang, Yu; Qiu, David. - : arXiv, 2021 BASE Show details 7 Injecting Text in Self-Supervised Speech Pretraining ... Chen, Zhehuai; Zhang, Yu; Rosenberg, Andrew. - : arXiv, 2021 BASE Show details 8 Large-scale multilingual audio visual dubbing ... Yang, Yi; Shillingford, Brendan; Assael, Yannis. - : arXiv, 2020 BASE Show details 9 Speech Recognition with Augmented Synthesized Speech ... Rosenberg, Andrew; Zhang, Yu; Ramabhadran, Bhuvana. - : arXiv, 2019 BASE Show details 10 Bytes are All You Need: End-to-End Multilingual Speech Recognition and Synthesis with Bytes ... Li, Bo; Zhang, Yu; Sainath, Tara. - : arXiv, 2018 BASE Show details 11 Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data ... Hsu, Wei-Ning; Zhang, Yu; Glass, James. - : arXiv, 2017 BASE Show details

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