or mel-spectogram [507], network architecture [108, 325], temporal pooling strategies [384], and
loss functions [91, 505, 569]. In the following subsection, we introduce two representative deep
embeddings: 𝑑-vector [552] and 𝑥-vector [506, 507]. These embeddings have been widely adopted
recently and have demonstrated state-of-the-art performance in various speaker-recognition tasks.
By understanding the strengths and weaknesses of different deep learning-based techniques for
speaker-representation learning, we can better leverage their power to improve the accuracy and
robustness of speaker-recognition systems.
AReviewofDeepLearningTechniquesforSpeechProcessing