Unveiling the Future of Speaker Diarization- A Comprehensive Review of Recent Deep Learning Breakthroughs

A Review of Speaker Diarization: Recent Advances with Deep Learning

Speaker diarization, a crucial task in the field of audio signal processing, involves the segmentation of an audio stream into individual speaker segments and assigning a unique label to each segment. Over the years, this task has seen significant advancements, especially with the integration of deep learning techniques. This article provides a comprehensive review of recent advances in speaker diarization using deep learning.

The first section of this review discusses the fundamental concepts of speaker diarization, including its applications and challenges. We then delve into the traditional methods used for speaker diarization, such as clustering-based and template-based approaches. These methods, although effective to some extent, have limitations in handling complex audio scenes with overlapping speakers.

The next section focuses on the rise of deep learning in speaker diarization. We explore various deep learning architectures that have been successfully applied to this task, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models. These architectures have shown remarkable performance improvements over traditional methods, especially in scenarios with overlapping speakers and varying speaking rates.

One of the key contributions of deep learning to speaker diarization is the introduction of end-to-end systems. These systems combine the speaker diarization task with other related tasks, such as speaker verification and speech recognition, into a single end-to-end framework. This integration not only improves the overall performance but also reduces the computational complexity.

Another significant development in deep learning-based speaker diarization is the use of multi-task learning. By training the model on multiple related tasks simultaneously, we can leverage the shared representations and improve the performance of each individual task. This approach has been particularly effective in scenarios where the speaker diarization task is combined with speaker verification.

Additionally, we discuss the challenges and limitations of deep learning-based speaker diarization, such as the need for large amounts of labeled data, computational complexity, and the difficulty of handling non-stationary audio signals. We also explore the potential solutions to these challenges, such as transfer learning, few-shot learning, and domain adaptation.

In the final section, we provide a summary of the key findings and future directions in deep learning-based speaker diarization. We highlight the importance of incorporating domain-specific knowledge and exploring new architectures to further improve the performance of speaker diarization systems.

In conclusion, this review of speaker diarization: recent advances with deep learning provides a comprehensive overview of the latest advancements in this field. As deep learning continues to evolve, we can expect further improvements in the accuracy and efficiency of speaker diarization systems, leading to more robust and practical applications in real-world scenarios.