Investigation on cross- and multilingual MLP features under matched and mismatched acoustical conditions

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Multilingual Speech Recognition with a Single End-to-End Model (2017, methodology): “Alternatively, multilingual bottleneck features from a DNN feature extractor can be used for either a Gaussian Mixture Model (GMM) or DNN-based systems [14].”
An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation (2017, methodology): “Speech recognition systems built with multilingual deep neural networks (DNNs) have been shown to provide consistent advantages especially for lowresourced languages [1, 2, 3, 4].”
Multilingual deep neural network based acoustic modeling for rapid language adaptation (2014, methodology): “INTRODUCTION HMM/DNN hybrid systems that use deep neural networks (DNNs) to estimate the emission probabilities of the hidden Markov model (HMM) states [1–4] were successfully applied to large vocabulary continous speech recognition (LVCSR) and led to a significant improvement in various tasks with…”
End-to-End Speech Recognition: A Survey (2023, unknown): “Pretraining approaches utilizing untranscribed audio, large-scale semi-supervised data and/or multilingual data [120], [121], [122], [123], [124], [125], [126], [127], [128], [129], [130], [131] would deserve”
Speech recognition and keyword spotting for low-resource languages: Babel project research at CUED (2014, background): “An alternative approach is to make use of data from other languages [6, 19, 20, 21], multi-language resources.”