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Smoothing the acoustic spectral time series of speech signals for noise reduction.

, , , and . ICCE-TW, page 1-2. IEEE, (2019)

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Subband Feature Statistics Normalization Techniques Based on a Discrete Wavelet Transform for Robust Speech Recognition., and . IEEE Signal Process. Lett., 16 (9): 806-809 (2009)Leveraging gain normalization for sub-band temporal features in noise-robust speech recognition., and . FSKD, page 1409-1412. IEEE, (2012)Optimization of Temporal Filters in the Modulation Frequency Domain via Constrained Linear Discriminant Analysis (C-LDA) for Constructing Robust Features in Speech Recognition.. ICASSP (4), page 805-808. IEEE, (2007)Cepstral Statistics Compensation Using Online Pseudo Stereo Codebooks for Robust Speech Recognition in Additive Noise Environments.. ICASSP (1), page 513-516. IEEE, (2006)Enhancing the Magnitude Spectrum of Speech Features for Robust Speech Recognition., , and . EURASIP J. Adv. Signal Process., (2012)Robust Speech Recognition via Enhancing the Complex-Valued Acoustic Spectrum in Modulation Domain., , and . IEEE ACM Trans. Audio Speech Lang. Process., 24 (2): 236-251 (2016)Time-Reversal Enhancement Network With Cross-Domain Information for Noise-Robust Speech Recognition., , , and . IEEE Multim., 29 (1): 114-124 (2022)Speech enhancement based on the integration of fully convolutional network, temporal lowpass filtering and spectrogram masking., , , and . ROCLING, page 226-240. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), (2019)Exploiting the compressed spectral loss for the learning of the DEMUCS speech enhancement network., , and . ROCLING, page 100-106. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), (2022)最小變異數調變頻譜濾波器於強健性語音辨識之研究 (A Study of Minimum Variance Modulation Filter for Robust Speech Recognition) In Chinese., , and . ROCLING, Association for Computational Linguistics and Chinese Language Processing (ACLCLP), Taiwan, (2010)