Feature extraction for robust speech recognition using a power-law nonlinearity and power-bias subtraction

This paper presents a new robust feature extraction algorithm based on a modified approach to power bias subtraction combined with applying a threshold to the power spectral density. Power bias level is selected as a level above which the signal power distribution is sharpest. The sharpness is measured using the ratio of arithmetic mean to the geometric mean of medium-duration power. When subtracting this bias level, power flooring is applied to enhance robustness. These new ideas are employed to enhance our recently introduced feature extraction algorithm PNCC (Power Normalized Cepstral Coefficient). While simpler than our previous PNCC, experimental results show that this new PNCC is showing better performance than our previous implementation. Index Terms — Robust speech recognition, physiological modeling, sharpness of power distribution, power flooring, auditory threshold 1.