Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on intelligent systems are presented i n a new report. According to news originating from Karnataka, India, by NewsRx e ditors, the research stated, "One of the most significant challenges in the real time speech processing applications is the elimination of noise in the corrupte d speech data. This noise can significantly impact the efficacy/performance of t he applications of speech processing." The news journalists obtained a quote from the research from Manipal Academy of Higher Education: "However, developing a robust noise reduction algorithm is cru cial for improving the accuracy of automatic speech recognition (ASR) and other speech processing systems under uncontrolled conditions. In this paper, we propo se an algorithm to reduce the background noise in the degraded speech data under highly nonstationary conditions. The proposed optimal smoothing and minima con trolled (OSMC) technique uses recursive averaging to enhance degraded speech dat a. Initially, a smoothed periodogram and local minima of the degraded speech dat a are computed and determined the time-frequency dependent threshold factor. The ratio of smoothed periodogram to local minima is used to find the active region s of speech in the degraded speech data by adapting the Bayesian minimum cost de cision rule. To calculate the estimated noise spectrum for each frequency bin, a time-frequency smoothing factors are used."
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