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 Kalman De-noising - Tracking and Filtering the Frequency Trajectories (RASTA) - How Kalman Filter is applied to de-noising problem - Advantages of Kalman

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

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.

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

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

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

De-noising the speech by filtering frequency trajectories

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

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

Kalman Filter adaptively updates itself with noise covariance Kalman Filtering Kalman Filter adaptively updates itself with noise covariance

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

A more informed noise reduction Advantages of Kalman 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