A Review of Speaker Diarization Recent Advances with Deep Learning
Speaker diarization, the task of segmenting an audio stream into homogeneous segments corresponding to different speakers, has been a long-standing challenge in the field of speech processing. With the advent of deep learning, significant advancements have been made in this area. This article provides a comprehensive review of the recent advances in speaker diarization using deep learning techniques.
The first section of this article discusses the background and motivation behind speaker diarization. It highlights the importance of this task in various applications, such as automatic speech recognition, speaker verification, and multimedia indexing. The next section provides an overview of the traditional speaker diarization methods, which rely on handcrafted features and clustering algorithms.
Moving on to the main focus of this article, we delve into the recent advances in speaker diarization using deep learning. The third section discusses the various deep learning architectures that have been employed for this task, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models. Each of these architectures has its unique strengths and weaknesses, and we analyze their performance in different scenarios.
The fourth section explores the use of deep learning for acoustic modeling in speaker diarization. Acoustic modeling is crucial for accurately identifying and segmenting speakers in an audio stream. We discuss the different deep learning-based acoustic models, such as Deep Belief Networks (DBNs), Long Short-Term Memory (LSTM) networks, and Variational Autoencoders (VAEs), and their impact on the overall performance of speaker diarization systems.
The fifth section focuses on the fusion of multiple modalities in speaker diarization using deep learning. Multimodal approaches, which combine acoustic, visual, and other sources of information, have shown promising results in various tasks. We discuss the recent advancements in multimodal speaker diarization, including the integration of facial images, text, and other relevant data sources.
The sixth section presents a comparative analysis of the performance of different deep learning-based speaker diarization systems. We evaluate the performance of these systems on various benchmark datasets and discuss the factors that contribute to their success or failure. Additionally, we highlight the challenges and limitations of current deep learning-based speaker diarization methods.
The final section of this article provides insights into the future directions of research in speaker diarization with deep learning. We discuss the potential of transfer learning, domain adaptation, and explainable AI in improving the performance and robustness of speaker diarization systems. Furthermore, we explore the opportunities for collaboration between academia and industry to address the practical challenges of speaker diarization in real-world applications.
In conclusion, this article offers a comprehensive review of the recent advances in speaker diarization using deep learning techniques. By exploring the various architectures, acoustic models, and multimodal approaches, we gain a deeper understanding of the challenges and opportunities in this field. As deep learning continues to evolve, we can expect further advancements in speaker diarization, paving the way for more efficient and accurate speech processing systems.
