Conceptual model of the technology for calculating the similarity threshold of two audio sequences
DOI:
https://doi.org/10.18664/ikszt.v29i3.313703Ключові слова:
machine learning, mfcc, dtw, feature extraction, speaker recognition, classification, voice cloning, siamese neural networksАнотація
The paper is focused on the pressing problem of speaker verification by means of voice time series comparison. The aim of this paper is to determine the orders of mel-frequency cepstral coefficients that most accurately describe the difference, between an authentic voice and an artificially generated copy for their further use as input to a neural network model in a resource-limited environment. To achieve this goal, the following tasks were accomplished: a conceptual model of the technology for determining the similarity threshold of two audio series was developed; the orders of fine-frequency cepstral coefficients with the most characteristic differences between the recording and the generated voice were determined on the basis of neural network analysis; an experimental study of the dependence of the execution time and computational load on the created feature vector when assessing the degree of similarity of two time series was conducted; and the optimal similarity threshold was determined on the basis of the chosen dataset. The developed model of the technology for determining the similarity threshold was tested on a dataset that is a combination of the DEEP-VOICE dataset and our own dataset. The demonstrated result of applying the developed technology showed an increase of 43% when using the specified MFCCs compared to using all of them. Based on experimental studies, the DTW acceptance threshold was set at 0.37.
Посилання
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