Speech enhancement with MAP estimation and ICA-based speech features

Jong Hwan Lee; Ho Young Jung; Te Won Lee; Soo Young Lee

doi:10.1049/el:20001028

Speech enhancement with MAP estimation and ICA-based speech features

Jong Hwan Lee, Ho Young Jung, Te Won Lee, Soo Young Lee

School of Electronics Engineering

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

The independent component analysis (ICA)-based speech features were used to remove Gaussian noise in the noisy speech signals using a maximum a posteriori (MAP) estimator. Speech signals corrupted by additive white Gaussian noise were recovered with greatly improved signal-to-noise ratio (SNR) values. Denoised spectral features also resulted in better recognition rates than the standard MFCC features.

Original language	English
Pages (from-to)	1506-1507
Number of pages	2
Journal	Electronics Letters
Volume	36
Issue number	17
DOIs	https://doi.org/10.1049/el:20001028
State	Published - 17 Aug 2000

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10.1049/el:20001028

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title = "Speech enhancement with MAP estimation and ICA-based speech features",

abstract = "The independent component analysis (ICA)-based speech features were used to remove Gaussian noise in the noisy speech signals using a maximum a posteriori (MAP) estimator. Speech signals corrupted by additive white Gaussian noise were recovered with greatly improved signal-to-noise ratio (SNR) values. Denoised spectral features also resulted in better recognition rates than the standard MFCC features.",

author = "Lee, \{Jong Hwan\} and Jung, \{Ho Young\} and Lee, \{Te Won\} and Lee, \{Soo Young\}",

year = "2000",

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T1 - Speech enhancement with MAP estimation and ICA-based speech features

AU - Lee, Jong Hwan

AU - Jung, Ho Young

AU - Lee, Te Won

AU - Lee, Soo Young

PY - 2000/8/17

Y1 - 2000/8/17

N2 - The independent component analysis (ICA)-based speech features were used to remove Gaussian noise in the noisy speech signals using a maximum a posteriori (MAP) estimator. Speech signals corrupted by additive white Gaussian noise were recovered with greatly improved signal-to-noise ratio (SNR) values. Denoised spectral features also resulted in better recognition rates than the standard MFCC features.

AB - The independent component analysis (ICA)-based speech features were used to remove Gaussian noise in the noisy speech signals using a maximum a posteriori (MAP) estimator. Speech signals corrupted by additive white Gaussian noise were recovered with greatly improved signal-to-noise ratio (SNR) values. Denoised spectral features also resulted in better recognition rates than the standard MFCC features.

UR - https://www.scopus.com/pages/publications/0034245955

U2 - 10.1049/el:20001028

DO - 10.1049/el:20001028

M3 - Article

AN - SCOPUS:0034245955

SN - 0013-5194

VL - 36

SP - 1506

EP - 1507

JO - Electronics Letters

JF - Electronics Letters

IS - 17

ER -

Speech enhancement with MAP estimation and ICA-based speech features

Abstract

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