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All Journal Journal of ICT Research and Applications Indonesian Journal of Artificial Intelligence and Data Mining JURNAL NUSANTARA APLIKASI MANAJEMEN BISNIS Jurnal Telematika ACADEMICS IN ACTION Journal of Community Empowerment JISA (Jurnal Informatika dan Sains) Jurnal Sains Indonesia IT For Society : Journal of Information Technology
Tjong Wan Sen
Universitas Presiden
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Journal : Journal of ICT Research and Applications

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Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients Tjong Wan Sen; Bambang Riyanto Trilaksono; Arry Akhmad Arman; Rila Mandala
Journal of ICT Research and Applications Vol. 3 No. 2 (2009)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2009.3.2.4

Abstract

To improve the performance of phoneme based Automatic Speech Recognition (ASR) in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT) coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited) and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA). These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4) from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments.
Co-Authors Abdul Ghofir Arry Akhmad Arman Bambang Riyanto Trilaksono Darien Daud Earlliawand Diky Djafar Sidik Eko Wahyudi Muhamad Ghofur Muhamad Muhaimin Rachma Oktari Riady, Sasmitoh Rahmad Rila Mandala Rosalina Rosalina Sahuri, Genta Sahuri Sinung Suakanto Siregar, Amril Mutoi Siti Aisyah Naili Mutia Yahya Suryana