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Introduction:

Speaker diarization is the process of partitioning an audio stream into homogeneous segments associated with each speaker. It plays a crucial role in various applications such as speech recognition, speaker identification, and speaker verification. In recent years, there has been a growing interest in multi-speaker segmentation, where the goal is to segment an audio stream containing multiple speakers into individual speaker segments. This thesis focuses on the development of a speaker diarization system for multi-speaker segmentation.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Introduction to Speaker diarization
2.2 Approaches to Speaker Diarization
2.3 Speaker Diarization Evaluation Metrics
2.4 Challenges in Multi-speaker Segmentation
2.5 State-of-the-art Speaker Diarization Systems
2.6 Speaker Diarization Datasets
2.7 Speaker Diarization Applications
2.8 Speaker Diarization in Real-world Scenarios
2.9 Speaker Diarization in Speech Recognition
2.10 Speaker Diarization in Speaker Verification

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Preprocessing
3.3 Speaker Embedding Extraction
3.4 Speaker Clustering
3.5 Diarization Segmentation
3.6 Speaker Turn Taking Detection
3.7 Speaker Overlap Detection
3.8 System Evaluation
3.9 Error Analysis

Chapter 4: System Implementation
4.1 Implementation Environment
4.2 Data Collection
4.3 Feature Extraction
4.4 Clustering Algorithm
4.5 Evaluation Metrics Implementation
4.6 System Optimization
4.7 Performance Analysis
4.8 Comparison with Existing Systems

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Future Work
5.4 Conclusion

Thesis Overview:

Speaker diarization is a vital task in the field of speech processing, particularly in scenarios where multiple speakers are present. This thesis focuses on developing a speaker diarization system for multi-speaker segmentation. Chapter 1 provides an introduction to the study, outlining the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 presents a comprehensive literature review on speaker diarization, including approaches, evaluation metrics, challenges, state-of-the-art systems, datasets, applications, and real-world scenarios.

Chapter 3 details the system design and methodology, covering aspects such as system architecture, data preprocessing, speaker embedding extraction, clustering, diarization segmentation, turn-taking detection, overlap detection, evaluation, and error analysis. Chapter 4 focuses on the system implementation, discussing the implementation environment, data collection, feature extraction, clustering algorithm, evaluation metrics, system optimization, performance analysis, and comparison with existing systems.

Chapter 5 concludes the thesis, summarizing the findings, highlighting the contributions of the study, outlining future work, and providing a conclusion. The thesis aims to contribute to the field of speaker diarization for multi-speaker segmentation and provide insights for further research in this area.

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