TY - GEN
T1 - Deep Denoising Autoencoder Based Post Filtering for Speech Enhancement
AU - Zezario, Ryandhimas E.
AU - Huang, Jen Wei
AU - Lu, Xugang
AU - Tsao, Yu
AU - Hwang, Hsin Te
AU - Wang, Hsin Min
N1 - Funding Information:
The authors would like to thank the Ministry of Science and Technology for providing financial supports (105-2221-E-006 -212 -MY2, 106-2221-E-001-017-MY2, and 107-2221-E-001-012-MY2).
Publisher Copyright:
© 2018 APSIPA organization.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In this paper, we present a simple yet effective deep denoising autoencoder (DDAE) based post-filter (DPF) approach for speech enhancement (SE). The DPF is designed to estimate the spectral difference of clean-noisy speech pair based on the enhanced-noisy speech pair. The difference estimated by the DPF approach is then used to compensate the noisy speech to obtain the final enhanced speech. We integrate the proposed DPF approach with one traditional SE method (minimum mean square error) and one deep-learning-based SE method (DDAE). Experiments on various noise types and signal-to-noise-ratio conditions were carried out to test the integrated systems. Results of three standardized objective evaluation metrics and automatic speech recognition (ASR) tests confirm that integrating the proposed DPF can improve the performance in further reducing spectral distortions and enhancing the speech quality and intelligibility.
AB - In this paper, we present a simple yet effective deep denoising autoencoder (DDAE) based post-filter (DPF) approach for speech enhancement (SE). The DPF is designed to estimate the spectral difference of clean-noisy speech pair based on the enhanced-noisy speech pair. The difference estimated by the DPF approach is then used to compensate the noisy speech to obtain the final enhanced speech. We integrate the proposed DPF approach with one traditional SE method (minimum mean square error) and one deep-learning-based SE method (DDAE). Experiments on various noise types and signal-to-noise-ratio conditions were carried out to test the integrated systems. Results of three standardized objective evaluation metrics and automatic speech recognition (ASR) tests confirm that integrating the proposed DPF can improve the performance in further reducing spectral distortions and enhancing the speech quality and intelligibility.
UR - http://www.scopus.com/inward/record.url?scp=85063430943&partnerID=8YFLogxK
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U2 - 10.23919/APSIPA.2018.8659598
DO - 10.23919/APSIPA.2018.8659598
M3 - Conference contribution
AN - SCOPUS:85063430943
T3 - 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
SP - 373
EP - 377
BT - 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
Y2 - 12 November 2018 through 15 November 2018
ER -