Multilingual deep neural network based acoustic modeling for rapid language adaptation
By Ngoc Thang Vu, David Imseng, Daniel Povey, Petr Motlícek, Tanja Schultz, Hervé Bourlard. 2014
138 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Speech & Audio, Meta & Continual Learning, Signal Processing
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Citations in the corpus
- Multilingual Speech Recognition with a Single End-to-End Model (2017, background): “Some of the multilingual AMs require a common phone set [3, 4, 13] while others share some of the acoustic model parameters [9–11, 15].”
- An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation (2017, background): “The target of the multilingual DNN can be either the universal International Phonetic Alphabet (IPA) based multilingual senones [6] or a layer consisting of separate activations for each language [3, 7, 8].”
- Multi-Dialect Speech Recognition with a Single Sequence-to-Sequence Model (2017, background): “The usual approach has been to define a common set of universal phone models [7, 8, 9] with appropriate parameter sharing [6] and train it on data from many languages with eventual adaptation on the data from the language of interest [10, 11, 12, 13].”
- Sequence-Based Multi-Lingual Low Resource Speech Recognition (2018, background): “Other approaches [17, 18] constructed a shared language independent phone set, which could then also be adapted to the target language.”
- Transfer Learning of Language-independent End-to-end ASR with Language Model Fusion (2018, background): “There are various approaches to leverage other languages: (a) to train a model multilingually (multi-task learning with other languages), and then further fine-tune to a particular language [6], and (b) to adapt a multilingual model to a new language using transfer learning [6–9] and additional feat”
- Multitask Learning of Deep Neural Networks for Low-Resource Speech Recognition (2015, background): “A basic assumption behind these techniques is that there exist good mappings between phones in some rich-resource languages and the phones in the low-resource target language so that transfer learning may be applied to transfer the knowledge learned in the former to the latter by adaptation [23], [2”