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    Articulatory-to-Acoustic Conversion Using BiLSTM-CNN Word-Attention-Based Method by Guofeng Ren, Guicheng Shao, Jianmei Fu

    Published 2020-01-01
    “…In this paper, we used the electromagnetic articulography (EMA) database designed by Taiyuan University of Technology, which contains ten speakers’ 299 disyllables and sentences of Mandarin, and extracted 8-dimensional articulatory features and 1-dimensional semantic feature relying on the word-attention layer; we then trained 200 samples and tested 99 samples for the articulatory-to-acoustic conversion. …”
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