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  • Ký hiệu PL/XG: 621
    Nhan đề: Improving Bottleneck Features For Vietnamese Large Vocabulary Continuous Speech Recognition System Using Deep Neural Networks /

DDC 621
Tác giả CN Quoc Bao Nguyen
Nhan đề Improving Bottleneck Features For Vietnamese Large Vocabulary Continuous Speech Recognition System Using Deep Neural Networks / Quoc Bao Nguyen, Tat Thang Vu, Chi Mai Huong
Mô tả vật lý 10 p.
Tóm tắt In this paper, the pre-training method based on denoising auto-encoder is investigated and proved to be good models for initializing bottleneck networks of Vietnamese speech recognition system that result in better recognition performance compared to base bottleneck features reported previously. The experiments are carried out on the dataset
Từ khóa tự do Neural network
Từ khóa tự do Deep bottleneck features
Từ khóa tự do Vietnmnese speech recognition
Tác giả(bs) CN Chi Mai Huong
Tác giả(bs) CN Tat Thang Vu
Nguồn trích Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics 2015P. 267-276 Số: 31 Tập: 4
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082 |a621
100 |aQuoc Bao Nguyen
245 |aImproving Bottleneck Features For Vietnamese Large Vocabulary Continuous Speech Recognition System Using Deep Neural Networks / |cQuoc Bao Nguyen, Tat Thang Vu, Chi Mai Huong
300 |a10 p.
520 |aIn this paper, the pre-training method based on denoising auto-encoder is investigated and proved to be good models for initializing bottleneck networks of Vietnamese speech recognition system that result in better recognition performance compared to base bottleneck features reported previously. The experiments are carried out on the dataset
653 |aNeural network
653 |aDeep bottleneck features
653 |aVietnmnese speech recognition
700 |aChi Mai Huong
700 |aTat Thang Vu
773 |tTạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics |d2015|gP. 267-276|v4|i31
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