NDLI: Robust speech recognition system using bidirectional Kalman filter

Content Provider	IET Digital Library
Author	Goh, Yeh Huann Raveendran, Paramesran Goh, Yann Ling
Abstract	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.
Starting Page	491
Ending Page	497
Page Count	7
ISSN	17519675
Volume Number	9
e-ISSN	17519683
Issue Number	Issue 6, Aug (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/9/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2014.0109
Journal	IET Signal Processing
Publisher Date	2015-07-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	AR Coefficient Autoregressive Process Autoregressive Processes Bidirectional Kalman Filter Constant Matrix Difference Equation Differential Equation (numerical Analysis) Filtering Method in Signal Processing Filtering Process Hidden Markov Model Interpolation And Function Approximation Kalman Filter Linear Algebra Markov Processes Matrix Algebra Mean Square Error Method Mean Squared Error Minimisation Measurement Equation Numerical Analysis Robust Speech Recognition System Speech Enhancement Speech Processing System Speech Processing Technique Speech Quality Enhancement Speech Recognition Speech Recognition And Synthesis Speech Signal State Estimation State-Space Domain System Dynamics Model
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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