A Low-Complexity Spectro-Temporal Distortion Measure for Audio Intelligibility Improvement

A speech pre-processing algorithm is presented to improve the speech intelligibility in noise for the near-end listener. The algorithm improves the intelligibility by optimally redistributing the speech energy over time and frequency for a perceptual distortion measure, which is based on a spectro-temporal auditory model. In contrast to spectral-only models, short-time information is taken into account. As a consequence, the algorithm is more sensitive to transient regions, which will therefore receive more amplification compared to stationary vowels. It is known from literature that changing the vowel-transient energy ratio is beneficial for improving speech intelligibility in noise. Objective intelligibility prediction results show that the proposed method has higher speech intelligibility in noise compared to other reference methods, without modifying the global speech energy. The Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. The Kalman filter is a mathematical power tool that is playing an increasingly important role in computer graphics as we include sensing of the real world in our systems. , electrical engineers are picturing the Kalman filter as a design tool for speech, whereby it can estimate and resolve errors that are contained in speech after passing through a distorted channel. Due to this motivating fact, there are many ways a Kalman filter can be tuned to suit engineering applications such as network telephony and even satellite phone conferencing.