UI - Tesis Membership :: Kembali

UI - Tesis Membership :: Kembali

Spectrogram sebagai fitur untuk convolutional neural network dalam pengembangan pengenal wicara bahasa indonesia berbasis hidden markov model = Spectrogram as a feature for convolutional neural network in the development of hidden markov model based bahasa indonesia speech recognition / Agung Santosa

Agung Santosa; Manurung, Hisar Maruli, supervisor; Indra Budi, examiner; Mohamad Ivan Fanany, supervisor; Muhammad Rahmat Widyanto, examiner ([Publisher not identified] , 2015)

 Abstrak

[ABSTRAK
Pesatnya perkembangan Deep Learning akhir-akhir ini juga menyentuh ASR
berbasis HMM, sehingga memunculkan teknik hibrid HMM-ANN. Salah satu
teknik Deep Learning yang cukup menjanjikan adalah penggunaan arsitektur
CNN. CNN yang memiliki kemampuan mendeteksi local correlation sesuai
untuk digunakan pada data spectrum suara. Spectrogram memiliki karakteristik
local correlation yang nampak secara visual. Penelitian ini adalah eksperimen
penggunaan spectrogram sebagai fitur untuk HMM-CNN untuk meningkatkan
kinerja ASR berbasis HMM. Penelitian menyimpulkan spectogram dapat
digunakan sebagai fitur untuk HMM-CNN untuk meningkatkan kinerja ASR
berbasis HMM.

ABSTRACT
The latest surge in Deep Learning affecting HMM based ASR, which give birth to
hybrid HMM-ANN technique. One of the promising Deep Learning technique is
the implementation of CNN architecture. The ability of CNN to detect local
correlation make it suitable to be used for speech spectral data. Spectrogram as a
speech spectral data has local correlation characteristic which is visually
observable. This research is an experiment to use spectrogram as a feature for
HMM-CNN to add to the performance of HMM based ASR. This research found
that spectrogram is indeed can be used as a feature for CNN to add to the
performance of HMM based ASR., The latest surge in Deep Learning affecting HMM based ASR, which give birth to
hybrid HMM-ANN technique. One of the promising Deep Learning technique is
the implementation of CNN architecture. The ability of CNN to detect local
correlation make it suitable to be used for speech spectral data. Spectrogram as a
speech spectral data has local correlation characteristic which is visually
observable. This research is an experiment to use spectrogram as a feature for
HMM-CNN to add to the performance of HMM based ASR. This research found
that spectrogram is indeed can be used as a feature for CNN to add to the
performance of HMM based ASR.]

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 Metadata

Jenis Koleksi : UI - Tesis Membership
No. Panggil : T43862
Entri utama-Nama orang :
Entri tambahan-Nama orang :
Entri tambahan-Nama badan :
Program Studi :
Subjek :
Penerbitan : [Place of publication not identified]: [Publisher not identified], 2015
Bahasa : ind
Sumber Pengatalogan : LibUI ind rda
Tipe Konten : text
Tipe Media : unmediated ; computer
Tipe Carrier : volume ; online resource
Deskripsi Fisik : xiv, 90 pages : illustartion ; 28 cm + appendix
Naskah Ringkas :
Lembaga Pemilik : Universitas Indonesia
Lokasi : Perpustakaan UI, Lantai 3
  • Ketersediaan
  • Ulasan
  • Sampul
No. Panggil No. Barkod Ketersediaan
T43862 TERSEDIA
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Tidak ada ulasan pada koleksi ini: 20414573
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