Title
Robust Speech Recognition via Enhancing the Complex-Valued Acoustic Spectrum in Modulation Domain
Abstract
The purpose of this paper is to develop a novel speech feature extraction framework for independently compensating the real and imaginary acoustic spectra of speech signals in the modulation domain with the techniques of histogram equalization (HEQ) and non-negative matrix factorization (NMF). By doing so, we can enhance not only the magnitude but also the phase components of the acoustic spectra, thereby creating noise-robust speech features. More specifically, the proposed framework makes the following three major contributions: First, via either of the HEQ and NMF operations, the long-term cross-frame correlation among the acoustic spectra at the same frequency can be captured to compensate for the spectral distortion caused by noise. Second, the noise effect can be handled in a high acoustic frequency resolution. Finally, the distortion dwelt in the acoustic spectra can be more extensively mitigated due to the independent processes for the respective real and imaginary parts. The evaluation experiments were carried out on the Aurora-2 and Aurora-4 benchmark tasks, and the corresponding results suggest that our proposed methods can achieve performance competitive to or better than many widely used noise robustness methods, including the well-known advanced front-end (AFE) extraction scheme, in speech recognition.
Year
DOI
Venue
2016
10.1109/TASLP.2015.2504781
Audio, Speech, and Language Processing, IEEE/ACM Transactions
Keywords
Field
DocType
Automatic speech recognition (ASR),feature extraction,histogram equalization (HEQ),modulation spectrum,noise robustness,non-negative matrix factorization (NMF)
Speech enhancement,Mel-frequency cepstrum,Pattern recognition,Computer science,Matrix decomposition,Speech recognition,Feature extraction,Robustness (computer science),Non-negative matrix factorization,Artificial intelligence,Histogram equalization,Distortion
Journal
Volume
Issue
ISSN
24
2
2329-9290
Citations 
PageRank 
References 
3
0.42
37
Authors
3
Name
Order
Citations
PageRank
Jeih-Weih Hung1144.14
Hsin-Ju Hsieh2113.64
Berlin Chen315134.59