The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kertas kerja ini membentangkan kaedah pengekstrakan ciri kuat hingar baru untuk pengecaman pertuturan. Ia adalah berdasarkan membuat kaedah anggaran spektrum kuasa Minimum Variance Distortionless Response (MVDR) teguh terhadap hingar. Kekukuhan ini diperoleh dengan mengubah suai kekangan tanpa herotan kaedah anggaran spektrum MVDR melalui pemberat nilai spektrum kuasa sub-jalur berdasarkan nisbah isyarat sub-jalur kepada bunyi. Pemberat optimum diperoleh dengan menggunakan penemuan eksperimen psikoakustik. Menurut eksperimen kami, teknik ini berjaya mengubah suai spektrum kuasa isyarat pertuturan dan menjadikannya teguh terhadap bunyi. Kaedah di atas, apabila dinilai pada tugas Aurora 2 untuk tujuan pengecaman, mengatasi kedua-dua ciri MFCC sebagai garis asas dan ciri berasaskan MVDR dalam keadaan bising yang berbeza.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Sanaz SEYEDIN, Seyed Mohammad AHADI, "A New Subband-Weighted MVDR-Based Front-End for Robust Speech Recognition" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 8, pp. 2252-2261, August 2010, doi: 10.1587/transinf.E93.D.2252.
Abstract: This paper presents a novel noise-robust feature extraction method for speech recognition. It is based on making the Minimum Variance Distortionless Response (MVDR) power spectrum estimation method robust against noise. This robustness is obtained by modifying the distortionless constraint of the MVDR spectral estimation method via weighting the sub-band power spectrum values based on the sub-band signal to noise ratios. The optimum weighting is obtained by employing the experimental findings of psychoacoustics. According to our experiments, this technique is successful in modifying the power spectrum of speech signals and making it robust against noise. The above method, when evaluated on Aurora 2 task for recognition purposes, outperformed both the MFCC features as the baseline and the MVDR-based features in different noisy conditions.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2252/_p
Salinan
@ARTICLE{e93-d_8_2252,
author={Sanaz SEYEDIN, Seyed Mohammad AHADI, },
journal={IEICE TRANSACTIONS on Information},
title={A New Subband-Weighted MVDR-Based Front-End for Robust Speech Recognition},
year={2010},
volume={E93-D},
number={8},
pages={2252-2261},
abstract={This paper presents a novel noise-robust feature extraction method for speech recognition. It is based on making the Minimum Variance Distortionless Response (MVDR) power spectrum estimation method robust against noise. This robustness is obtained by modifying the distortionless constraint of the MVDR spectral estimation method via weighting the sub-band power spectrum values based on the sub-band signal to noise ratios. The optimum weighting is obtained by employing the experimental findings of psychoacoustics. According to our experiments, this technique is successful in modifying the power spectrum of speech signals and making it robust against noise. The above method, when evaluated on Aurora 2 task for recognition purposes, outperformed both the MFCC features as the baseline and the MVDR-based features in different noisy conditions.},
keywords={},
doi={10.1587/transinf.E93.D.2252},
ISSN={1745-1361},
month={August},}
Salinan
TY - JOUR
TI - A New Subband-Weighted MVDR-Based Front-End for Robust Speech Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 2252
EP - 2261
AU - Sanaz SEYEDIN
AU - Seyed Mohammad AHADI
PY - 2010
DO - 10.1587/transinf.E93.D.2252
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2010
AB - This paper presents a novel noise-robust feature extraction method for speech recognition. It is based on making the Minimum Variance Distortionless Response (MVDR) power spectrum estimation method robust against noise. This robustness is obtained by modifying the distortionless constraint of the MVDR spectral estimation method via weighting the sub-band power spectrum values based on the sub-band signal to noise ratios. The optimum weighting is obtained by employing the experimental findings of psychoacoustics. According to our experiments, this technique is successful in modifying the power spectrum of speech signals and making it robust against noise. The above method, when evaluated on Aurora 2 task for recognition purposes, outperformed both the MFCC features as the baseline and the MVDR-based features in different noisy conditions.
ER -