TY - JOUR AB - 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. AU - Leutnant, Volker AU - Krueger, Alexander AU - Haeb-Umbach, Reinhold ID - 11861 IS - 1 JF - IEEE/ACM Transactions on Audio, Speech, and Language Processing KW - computational complexity KW - reverberation KW - speech recognition KW - automatic speech recognition KW - background noise KW - clean speech KW - computational complexity KW - energy compensation KW - logarithmic mel power spectral domain KW - mel frequency cepstral coefficients KW - microphone input signals KW - model-based feature compensation schemes KW - noisy reverberant speech automatic recognition KW - noisy reverberant speech features KW - reverberation KW - Atmospheric modeling KW - Computational modeling KW - Noise KW - Noise measurement KW - Reverberation KW - Speech KW - Vectors KW - Model-based feature compensation KW - observation model for reverberant and noisy speech KW - recursive observation model KW - robust automatic speech recognition SN - 2329-9290 TI - A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech VL - 22 ER - TY - CONF AB - In this contribution we derive the Maximum A-Posteriori (MAP) estimates of the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations. We assume the distortion to be white Gaussian noise of known mean and variance. An approximate conjugate prior of the GMM parameters is derived allowing for a computationally efficient implementation in a sequential estimation framework. Simulations on artificially generated data demonstrate the superiority of the proposed method compared to the Maximum Likelihood technique and to the ordinary MAP approach, whose estimates are corrected by the known statistics of the distortion in a straightforward manner. AU - Chinaev, Aleksej AU - Haeb-Umbach, Reinhold ID - 11740 KW - Gaussian noise KW - maximum likelihood estimation KW - parameter estimation KW - GMM parameter KW - Gaussian mixture model KW - MAP estimation KW - Map-based estimation KW - maximum a-posteriori estimation KW - maximum likelihood technique KW - noisy observation KW - sequential estimation framework KW - white Gaussian noise KW - Additive noise KW - Gaussian mixture model KW - Maximum likelihood estimation KW - Noise measurement KW - Gaussian mixture model KW - Maximum a posteriori estimation KW - Maximum likelihood estimation SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) TI - MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations ER -