The simulated human voice would equal the educated person's speech if the difference between two vectors is almost null. The Euclidean distance between the vectors will now be used to balance all vectors in order to distinguish the voice and voice. The same technique is then applied for calculating speech signals and constructing a test feature vector. First, the proposed methods are used for voice signals, and then we construct a vector train function that includes the derived low level function and estimated formant parameters. Here, we suggested a new text-related method for the identification of human voices (TDHVR) system, which utilizes the discrete wavelet transform (DWT) for low level feature extraction, Relative Spectral Algorithm (RSA) for denoising the voice signal and finally Additive Prognostication (AP) for estimating the formants. This study is an accessible and robust approach for obtaining voice recognition features. Waves are considered to be used to decode the speech signal more efficiently.
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