• Gaussian Mixture Models (GMMs): Gaussian Mixture Models (GMMs) are powerful genera-
tive models employed to represent the probability distribution of a speech feature vector.
They achieve this by combining multiple Gaussian distributions with different weights.
GMMs have found widespread applications in speaker identification [259] and speech recog-
nition tasks [461]. Specifically, in speaker identification, GMMs are utilized to capture the
distribution of speaker-specific features, enabling the recognition of individuals based on
their unique characteristics. Conversely, in speech recognition, GMMs are employed to
model the acoustic properties of speech sounds, facilitating accurate recognition of spoken
words and phrases. GMMs play a crucial role in these domains, enabling robust and efficient
analysis of speech-related data.
• Support Vector Machines (SVMs): Support Vector Machines (SVMs) are a widely adopted
AReviewofDeepLearningTechniquesforSpeechProcessing