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Speech Recognition in Noise

Speech Recognition in Noise. Esfandiar Zavarehei Department of Electronic and Computer Engineering Brunel University 25 May, 2004. Contents. The use of formant features in speech recognition - Variable-Order LP Formant Tracker with Kalman Filtering - Results

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Speech Recognition in Noise

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  1. Speech Recognition in Noise Esfandiar Zavarehei Department of Electronic and Computer Engineering Brunel University 25 May, 2004

  2. Contents The use of formant features in speech recognition - Variable-Order LP Formant Tracker with Kalman Filtering - Results Kalman De-noising - Tracking and Filtering the Frequency Trajectories (RASTA) - How Kalman Filter is applied to de-noising problem - Advantages of Kalman

  3. Variable-Order LP Formant tracker • Higher order of LP modelling for higher resolution • Continuity criteria for better classification • Kalman Filtering for smoother Tracks

  4. Formant Feature (FF) Vectors • In addition to the Frequency of poles their Band Widths and Magnitudes are used as well • The HMM models are trained on mono-phones.

  5. FF vs. MFCC with and without energy component Mono-phone recognition in Train noise • Better performance of FF in severe noisy conditions

  6. Robustness of dynamic FF to noise Mono-phone recognition in Train noise • Dynamic Features are much more robust to noise

  7. The use of the Formants for consonant recognition Mono-phone recognition in Train noise • Higher Recognition rates than vowels in higher SNR • More sensitive to noise because of the lower energy level

  8. De-noising the speech by filtering frequency trajectories

  9. RelAtive SpecTrA (RASTA) Processing • Filtering the frequency trajectories of the cubic root of power spectrum using a fixed IIR filter

  10. The use of FIR filters in RASTA • Filtering the frequency trajectories of the power spectrum using a bank of non-casual FIR filters • not adaptive • experimentally derived Filters’ Impulse Response

  11. Kalman Filtering • Kalman Filter adaptively updates itself with noise covariance

  12. Prior Noise Model and Trajectory Statistics Noise Modelling Noise Covariance Mean Error covariance Predictor Kalman Gain VAD Segment Frequency Bin Trajectory Predicted Estimator Output Spectral Subtraction Observation Kalman Filtering How Kalman Filter is applied to de-noising problem Noise Modelling and updating Neighbour Trajectory

  13. A more informed noise reduction Combining the prediction and the observation of the frequency trajectory Adaptively updating the noise model while filtering the trajectory (in comparison with RASTA) Could (and probably should) be combined with spectral subtraction for improved performance Advantages of Kalman

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