Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition - Windmann, Stefan; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this paper, parameter estimation of a state-space model of noise or noisy speech cepstra is investigated. A blockwise EM algorithm is derived for the estimation of the state and observation noise covariance from noise-only input data. It is supposed to be used during the offline training mode of a speech recognizer. Further a sequential online EM algorithm is developed to adapt the observation noise covariance on noisy speech cepstra at its input. The estimated parameters are then used in model-based speech feature enhancement for noise-robust automatic speech recognition. Experiments on the AURORA4 database lead to improved recognition results with a linear state model compared to the assumption of stationary noise.
Keywords: AURORA4 database; blockwise EM algorithm; covariance analysis; linear state model; noise covariance; noise-robust automatic speech recognition; noisy speech cepstra; offline training mode; parameter estimation; speech recognition; speech recognition equipment; speech recognizer; state-space methods; state-space model

Robust speaker direction estimation with particle filtering - Warsitz, Ernst; Haeb-Umbach, Reinhold (IEEE Workshop on Multimedia Signal Processing (MMSP 2004))
The paper is concerned with binaural signal processing for a bimodal human-robot interface with hearing and vision. The two microphone signals are processed to obtain an enhanced single-channel input signal for the subsequent speech recognizer and to localize the acoustic source, an important information for establishing a natural human-robot communication. We utilize a robust adaptive algorithm for filter-and-sum beamforming (FSB) and extract speaker direction information from the resulting FIR filter coefficients. Further, particle filtering is applied which conducts a nonlinear Bayesian tracking of speaker movement. Good location accuracy can be achieved even in highly reverberant environments. The results obtained outperform the conventional generalized cross correlation (GCC) method.
Keywords: bimodal human-robot interface; binaural signal processing; enhanced single-channel input signal; filter-and-sum beamforming; filtering theory; FIR filter coefficient; generalized cross correlation method; microphones; microphone signal; nonlinear Bayesian tracking; particle filtering; robust adaptive algorithm; robust speaker direction estimation; signal processing; speech enhancement; speech recognition; speech recognizer; user interfaces