This is achieved by passing the acoustic features extracted from the speech signal through
a deep neural network, such as a CNN or RNN[506].
• Clustering: In this component, the extracted speaker embeddings are clustered based on
similarity, and each cluster represents a different speaker [14, 536]. This process commonly
uses unsupervised clustering algorithms, such as k-means clustering.
• Speaker Classification: In this component, the speaker embeddings are classified into different
speaker identities using a supervised classification algorithm, such as SVM or MLP [14, 536].
• Re-segmentation: This component is responsible for refining the initial segmentation by
adjusting the segment boundaries based on the classification results. It helps in improving the
accuracy of speaker diarization by reducing the errors made during the initial segmentation.
Various studies focus on traditional speaker diarization systems [14, 536]. This paper will review
AReviewofDeepLearningTechniquesforSpeechProcessing