2.3 Traditional models for speech processing
Traditional speech representation learning algorithms based on shallow models utilize basic non-
parametric models for extracting features from speech signals. The primary objective of these
models is to extract significant features from the speech signal through mathematical operations,
such as Fourier transforms, wavelet transforms, and linear predictive coding (LPC). The extracted
features serve as inputs to classification or regression models. The shallow models aim to extract
meaningful features from the speech signal, enabling the classification or regression model to learn
and make accurate predictions.
AReviewofDeepLearningTechniquesforSpeechProcessing