Language-dependent state clustering for multilingual acoustic modelling

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Multilingual Speech Recognition with a Single End-to-End Model (2017, background): “…continuous speech recognizers are severly limited [1] These challenges have meant that there has been a perennial interest in multilingual and cross-lingual models which allow for knowledge transfer across languages, and thus relieve burdensome data requirements [2, 3, 4, 5, 6, 7, 8, 9, 10, 11].”
A study on multilingual acoustic modeling for large vocabulary ASR (2009, background): “One potential solution is to explore shared acoustic phonetic structures among different languages to build a large set of acoustic models (e.g., [1][2][3][5][7][8][9]) that characterize all the phone units needed in order to cover all the spoken languages being considered.”
Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers (2013, methodology): “, the IPA universal phone set [15][16]) or by data-driven clustering [15][16][17][18].”