Advanced Feature Extraction for Robust Speech Recognition Based on Maximizing the Sharpness of the Power Distribution

Advanced Feature Extraction for Robust Speech Recognition Based on Maximizing the Sharpness of the Power Distribution
Author(s):
Dr. B. C. Premkumar, Dr. Prasanna Kumar. C, Dr. Suresh D
Keywords:
Robust speech recognition, physiological modeling, sharpness of power distribution, power flooring, auditory threshold.
Abstract
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.
Article Details
Unique Paper ID: 154606 Publication Volume & Issue: Volume 8, Issue 11 Page(s): 341 - 343
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