access icon free Robust speech recognition system using bidirectional Kalman filter

Kalman filter is normally used to enhance speech quality in a noisy environment, in which the speech signals are usually modelled as autoregressive (AR) process, and represented in the state-space domain. It is a known fact that to identify the changing AR coefficients in every time state requires extensive computation. In this paper, the authors develop a bidirectional Kalman filter and apply it in a speech processing system. The proposed filter uses a system dynamics model that utilises the past and the future measurements to form an estimate of the system's current time state. It provides efficient recursive means to estimate the state of a process that minimises the mean of the squared error. Compared to the conventional Kalman filter, the proposed filter reduces the computation time in two ways: (i) by avoiding the computation of AR parameters in each time state, and (ii) by reducing the dimension of the matrices involved in the difference equations and the measurement equations into constant (1 × 1) matrices. The speech recognition result shows that the developed speech recognition system becomes more robust after the proposed filtering process, and the proposed filter's low computational expense makes it applicable in the practical hidden Markov model-based speech recognition system.

Inspec keywords: autoregressive processes; difference equations; mean square error methods; state estimation; Kalman filters; speech enhancement; hidden Markov models; speech recognition; matrix algebra

Other keywords: autoregressive process; difference equation; state estimation; AR coefficient; robust speech recognition system; speech signal; mean squared error minimisation; speech quality enhancement; hidden Markov model; measurement equation; constant matrix; filtering process; bidirectional Kalman filter; system dynamics model; state-space domain; speech processing system

Subjects: Differential equations (numerical analysis); Linear algebra (numerical analysis); Interpolation and function approximation (numerical analysis); Linear algebra (numerical analysis); Markov processes; Speech recognition and synthesis; Speech processing techniques; Markov processes; Filtering methods in signal processing; Differential equations (numerical analysis); Interpolation and function approximation (numerical analysis)

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