A new approach for the adaptation of HMMs to reverberation and background noise
Speech Communication
Enhanced speech features by single-channel joint compensation of noise and reverberation
IEEE Transactions on Audio, Speech, and Language Processing
Model-based feature enhancement for reverberant speech recognition
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
EVAM: an eigenvector-based algorithm for multichannel blinddeconvolution of input colored signals
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Precise Dereverberation Using Multichannel Linear Prediction
IEEE Transactions on Audio, Speech, and Language Processing
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
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In this contribution we present a theoretical and experimental investigation into the effects of reverberation and noise on features in the logarithmic mel power spectral domain, an intermediate stage in the computation of the mel frequency cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining insight into the complex interaction between clean speech, noise, and noisy reverberant speech features is essential for any ASR system to be robust against noise and reverberation present in distant microphone input signals. The findings are gathered in a probabilistic formulation of an observation model which may be used in model-based feature compensation schemes. The proposed observation model extends previous models in three major directions: First, the contribution of additive background noise to the observation error is explicitly taken into account. Second, an energy compensation constant is introduced which ensures an unbiased estimate of the reverberant speech features, and, third, a recursive variant of the observation model is developed resulting in reduced computational complexity when used in model-based feature compensation. The experimental section is used to evaluate the accuracy of the model and to describe how its parameters can be determined from test data.