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
Dalam kertas kerja ini, kami mencadangkan penindasan hingar yang kerumitan rendah dan tepat berdasarkan model SNR (Nisbah Pertuturan kepada Bunyi) apriori untuk keteguhan yang lebih besar wrt turun naik hingar jangka pendek. SNR a priori, nisbah spektrum pertuturan dan spektrum hingar dalam domain spektrum, mewakili perbezaan antara ciri pertuturan dan ciri hingar dalam domain ciri, termasuk domain mel-cepstral dan domain spektrum kuasa logaritma. Ini kerana operasi logaritma digunakan untuk penukaran domain. Oleh itu, model SNR a priori dengan mudah boleh dinyatakan dari segi perbezaan antara model pertuturan dan model hingar, yang dimodelkan oleh model campuran Gaussian, dan ia boleh dijana dengan kos pengiraan yang rendah. Dengan menggunakan SNR priori yang dianggarkan dengan tepat berdasarkan model SNR a priori, adalah mungkin untuk mengira pekali tepat penapis penindasan hingar dengan mengambil kira varians hingar, tanpa peningkatan serius dalam kos pengiraan berbanding kos berasaskan model konvensional. Penapis Wiener (MBW). Kami telah menjalankan penilaian pengecaman pertuturan dalam kereta menggunakan pangkalan data CENSREC-2, dan perbandingan kaedah yang dicadangkan dengan MBW konvensional menunjukkan bahawa kadar ralat pengecaman untuk semua persekitaran hingar telah dikurangkan sebanyak 9%, dan itu, terutamanya, untuk persekitaran bunyi bunyi dikurangkan sebanyak 11%. Kami menunjukkan bahawa kaedah yang dicadangkan boleh diproses dengan tahap sumber pengiraan dan ingatan yang rendah melalui pelaksanaan pada pemproses isyarat digital.
Masanori TSUJIKAWA
Kansai University,NEC Corporation
Yoshinobu KAJIKAWA
Kansai University
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Salinan
Masanori TSUJIKAWA, Yoshinobu KAJIKAWA, "Low-Complexity and Accurate Noise Suppression Based on an a Priori SNR Model for Robust Speech Recognition on Embedded Systems and Its Evaluation in a Car Environment" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 9, pp. 1224-1233, September 2023, doi: 10.1587/transfun.2022EAP1130.
Abstract: In this paper, we propose a low-complexity and accurate noise suppression based on an a priori SNR (Speech to Noise Ratio) model for greater robustness w.r.t. short-term noise-fluctuation. The a priori SNR, the ratio of speech spectra and noise spectra in the spectral domain, represents the difference between speech features and noise features in the feature domain, including the mel-cepstral domain and the logarithmic power spectral domain. This is because logarithmic operations are used for domain conversions. Therefore, an a priori SNR model can easily be expressed in terms of the difference between the speech model and the noise model, which are modeled by the Gaussian mixture models, and it can be generated with low computational cost. By using a priori SNRs accurately estimated on the basis of an a priori SNR model, it is possible to calculate accurate coefficients of noise suppression filters taking into account the variance of noise, without serious increase in computational cost over that of a conventional model-based Wiener filter (MBW). We have conducted in-car speech recognition evaluation using the CENSREC-2 database, and a comparison of the proposed method with a conventional MBW showed that the recognition error rate for all noise environments was reduced by 9%, and that, notably, that for audio-noise environments was reduced by 11%. We show that the proposed method can be processed with low levels of computational and memory resources through implementation on a digital signal processor.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022EAP1130/_p
Salinan
@ARTICLE{e106-a_9_1224,
author={Masanori TSUJIKAWA, Yoshinobu KAJIKAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Low-Complexity and Accurate Noise Suppression Based on an a Priori SNR Model for Robust Speech Recognition on Embedded Systems and Its Evaluation in a Car Environment},
year={2023},
volume={E106-A},
number={9},
pages={1224-1233},
abstract={In this paper, we propose a low-complexity and accurate noise suppression based on an a priori SNR (Speech to Noise Ratio) model for greater robustness w.r.t. short-term noise-fluctuation. The a priori SNR, the ratio of speech spectra and noise spectra in the spectral domain, represents the difference between speech features and noise features in the feature domain, including the mel-cepstral domain and the logarithmic power spectral domain. This is because logarithmic operations are used for domain conversions. Therefore, an a priori SNR model can easily be expressed in terms of the difference between the speech model and the noise model, which are modeled by the Gaussian mixture models, and it can be generated with low computational cost. By using a priori SNRs accurately estimated on the basis of an a priori SNR model, it is possible to calculate accurate coefficients of noise suppression filters taking into account the variance of noise, without serious increase in computational cost over that of a conventional model-based Wiener filter (MBW). We have conducted in-car speech recognition evaluation using the CENSREC-2 database, and a comparison of the proposed method with a conventional MBW showed that the recognition error rate for all noise environments was reduced by 9%, and that, notably, that for audio-noise environments was reduced by 11%. We show that the proposed method can be processed with low levels of computational and memory resources through implementation on a digital signal processor.},
keywords={},
doi={10.1587/transfun.2022EAP1130},
ISSN={1745-1337},
month={September},}
Salinan
TY - JOUR
TI - Low-Complexity and Accurate Noise Suppression Based on an a Priori SNR Model for Robust Speech Recognition on Embedded Systems and Its Evaluation in a Car Environment
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1224
EP - 1233
AU - Masanori TSUJIKAWA
AU - Yoshinobu KAJIKAWA
PY - 2023
DO - 10.1587/transfun.2022EAP1130
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2023
AB - In this paper, we propose a low-complexity and accurate noise suppression based on an a priori SNR (Speech to Noise Ratio) model for greater robustness w.r.t. short-term noise-fluctuation. The a priori SNR, the ratio of speech spectra and noise spectra in the spectral domain, represents the difference between speech features and noise features in the feature domain, including the mel-cepstral domain and the logarithmic power spectral domain. This is because logarithmic operations are used for domain conversions. Therefore, an a priori SNR model can easily be expressed in terms of the difference between the speech model and the noise model, which are modeled by the Gaussian mixture models, and it can be generated with low computational cost. By using a priori SNRs accurately estimated on the basis of an a priori SNR model, it is possible to calculate accurate coefficients of noise suppression filters taking into account the variance of noise, without serious increase in computational cost over that of a conventional model-based Wiener filter (MBW). We have conducted in-car speech recognition evaluation using the CENSREC-2 database, and a comparison of the proposed method with a conventional MBW showed that the recognition error rate for all noise environments was reduced by 9%, and that, notably, that for audio-noise environments was reduced by 11%. We show that the proposed method can be processed with low levels of computational and memory resources through implementation on a digital signal processor.
ER -