A New Technique for Perceptual Distortion Measure on A Spectro Temporal Auditory Model

Abstract

Speech processing is used widely in every day’s applications that most people take for granted, such as network wire lines, cellular telephony, telephony system and telephone answering machines. Due to its popularity and increasing of demand, engineers are trying various approaches of improving the process. One of the methods for improving is trying on different methods of filtering techniques. Thus, this instigates an introduction of a filtering technique known as Kalman filtering. In the early days, Kalman filtering was very popular in the research field of navigation because of its magnificent accurate estimation characteristic. Since then, electronic engineers manipulate its advantages to useful purpose in speech processing. Consequently, today it had become a popular filtering technique for estimating and resolving redundant errors containing in speech. 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 o 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.