| Summary: | 碩士 === 國立暨南國際大學 === 電機工程學系 === 102 === In this thesis, we exploit the technique of nonnegative matrix factorization (NMF) in
speech enhancement, considering the sub-band and temporal patch characteristics of noisy
spectrogram. We investigate the segmental NMF scheme in speech enhancement and
compare it with the conventional frame-wise counterpart. Two forms of segmental NMF
methods are investigated, which respectively decompose the spectrogram into temporal
and spectral segmental parts, and then compensates each segment to reduce the noise
effect.
We evaluate these NMF-based methods in a subset of the Aurora-2 connected digit
database. Experimental results show that these NMF-based methods can improve the
quality of noise-corrupted speech signals, and they are well additive to two well-known
methods, spectral subtraction (SS) and minimum mean-squared error (MMSE). We also
show that the speech signals enhanced by NMF-based methods can result in better
recognition accuracy relative to the original signals without enhancement.
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