Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Recognizing Reverberant Speech with RASTA - PLP
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Blind Model Selection for Automatic Speech Recognition in Reverberant Environments
Journal of VLSI Signal Processing Systems
Subspace methods for multimicrophone speech dereverberation
EURASIP Journal on Applied Signal Processing
A new approach for the adaptation of HMMs to reverberation and background noise
Speech Communication
Low delay noise reduction and dereverberation for hearing aids
EURASIP Journal on Advances in Signal Processing - Special issue on digital signal processing for hearing instruments
Integrated speech enhancement method using noise suppression and dereverberation
IEEE Transactions on Audio, Speech, and Language Processing
Enhanced speech features by single-channel joint compensation of noise and reverberation
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
EVAM: an eigenvector-based algorithm for multichannel blinddeconvolution of input colored signals
IEEE Transactions on Signal Processing
A Novel Uncertainty Decoding Rule With Applications to Transmission Error Robust Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
A two-stage algorithm for one-microphone reverberant speech enhancement
IEEE Transactions on Audio, Speech, and Language Processing
System Identification in the Short-Time Fourier Transform Domain With Crossband Filtering
IEEE Transactions on Audio, Speech, and Language Processing
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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In this paper, we present a new technique for automatic speech recognition (ASR) in reverberant environments. Our approach is aimed at the enhancement of the logarithmic Mel power spectrum, which is computed at an intermediate stage to obtain the widely used Mel frequency cepstral coefficients (MFCCs). Given the reverberant logarithmic Mel power spectral coefficients (LMPSCs), a minimum mean square error estimate of the clean LMPSCs is computed by carrying out Bayesian inference. We employ switching linear dynamical models as an a priori model for the dynamics of the clean LMPSCs. Further, we derive a stochastic observation model which relates the clean to the reverberant LMPSCs through a simplified model of the room impulse response (RIR). This model requires only two parameters, namely RIR energy and reverberation time, which can be estimated from the captured microphone signal. The performance of the proposed enhancement technique is studied on the AURORA5 database and compared to that of constrained maximum-likelihood linear regression (CMLLR). It is shown by experimental results that our approach significantly outperforms CMLLR and that up to 80% of the errors caused by the reverberation are recovered. In addition to the fact that the approach is compatible with the standard MFCC feature vectors, it leaves the ASR back-end unchanged. It is of moderate computational complexity and suitable for real time applications.