A Machine Learning Approach for Voice Modelling and Identification

The study of nonlinear and dynamic systems, as well as the field of practical systems, both make system identification extremely important. Because the vast majority of practical systems do not have any previous information on their behaviour, mathematical modelling is essential. To address this issue of system identification, the authors in this work suggest a Deep-Extreme Learning Machine (DELM) model trained on discrete wavelet transform coefficients. A voice model is considered, similar to the Autoregressive model (AR), for identification. The goal is to create a voice model using machine learning techniques and its identification with level-3 Haar DWT coefficients of the voice signal. The results of the comparison are presented in the result section. The mean square error (MSE) is the metric that is used to assess performance. The proposed DELM model outperforms the models developed with RNN, Bi-LSTM, and LPC-RBF. The findings of this work are able to improve prediction accuracy while reducing convergence time.