Multitask Learning of Deep Neural Networks for Low-Resource Speech Recognition

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How researchers see this paper

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Transfer Learning for Speech Recognition on a Budget (2017, background): “While this achieves some competitive results (Chen and Mak, 2015; Knill et al., 2014), it still requires large amounts of data to yield robust improvements (Heigold et al., 2013).”
A survey of deep neural network architectures and their applications (2017, methodology): “ASR using DNNs with no requirement on additional language re sources [20].”
An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation (2017, background): “The result is consistent with previous work [3, 9, 10].”
Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model (2019, background): “Successful strategies for building multilingual acoustic models (AMs) include stacked bottleneck features [1–4], shared hidden layers [5, 6], knowledge distillation [7], and multitask learning [8].”
Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition (2021, background): “There has been a great deal of attention to multi-lingual automatic speech recognition (ASR) that can recognize multiple languages [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20].”
A survey on deep learning for big data (2018, background): “Deep learning has achieved state-of-the-art performance in a broad of applications such as computer vision [15,16], speech recognition [17,18] and text understanding [19,20].”