Biorthogonal Wavelet Packet and Adaptive Filters for Noisy Speech Reduction

Minimizing noise in speech signals is crucial for applications such as speech recognition and enhancement. This paper proposes a hybrid technique that combines a biorthogonal wavelet packet with a Recursive Least Squares (RLS) adaptive filter to reduce environmental and colored noise during the preprocessing stage. Simulation results demonstrate a 2–10% improvement in speech signal strength under noisy conditions. The biorthogonal wavelet's vanishing moments and the length of the RLS filter play key roles in preserving speech characteristics while suppressing noise. Performance is evaluated using Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR) metrics, showing effective reduction of pink and babble noise across varying decibel levels, thereby ensuring enhancing the clarity of speech for recognition applications.