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http://dx.doi.org/10.13067/JKIECS.2021.16.3.465

Nonlinear Speech Enhancement Method for Reducing the Amount of Speech Distortion According to Speech Statistics Model  

Choi, Jae-Seung (Division of Smart Electrical and Electronic Engineering, Silla University)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.16, no.3, 2021 , pp. 465-470 More about this Journal
Abstract
A robust speech recognition technology is required that does not degrade the performance of speech recognition and the quality of the speech when speech recognition is performed in an actual environment of the speech mixed with noise. With the development of such speech recognition technology, it is necessary to develop an application that achieves stable and high speech recognition rate even in a noisy environment similar to the human speech spectrum. Therefore, this paper proposes a speech enhancement algorithm that processes a noise suppression based on the MMSA-STSA estimation algorithm, which is a short-time spectral amplitude method based on the error of the least mean square. This algorithm is an effective nonlinear speech enhancement algorithm based on a single channel input and has high noise suppression performance. Moreover this algorithm is a technique that reduces the amount of distortion of the speech based on the statistical model of the speech. In this experiment, in order to verify the effectiveness of the MMSA-STSA estimation algorithm, the effectiveness of the proposed algorithm is verified by comparing the input speech waveform and the output speech waveform.
Keywords
Speech Recognition; Speech Enhancement Algorithm; Noise Suppression; MMSA-STSA Estimation; Statistics Model;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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