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Table of contents
Acknowledgements
List of Figures
List of Tables
Glossary
1 Introduction
1.1 Domain robust and efficient speaker diarization
1.2 Low-latency speaker spotting
1.3 Contributions and thesis outline
Publications
2 Literature review
2.1 Acoustic feature extraction
2.2 Voice activity detection
2.3 Segmentation and speaker change detection
2.4 Speaker modelling
2.5 Clustering
2.6 Resegmentation
2.7 Evaluation and metrics
2.7.1 Speaker diarization
2.7.2 Speaker recognition
2.8 Summary
I Domain robust and efficient speaker diarization
3 Binary key speaker modelling: a review
3.1 Introduction
3.2 Binary key background model
3.2.1 Speaker recognition
3.2.2 Speaker diarization
3.3 Feature binarization
3.4 Segmental representations
3.5 Similarity metrics for binary key speaker modelling
3.6 Recent improvements and use cases
3.6.1 Recent improvements for speaker recognition
3.6.2 Recent improvements for speaker diarization
3.6.3 Other applications
3.7 Baseline system for speaker diarization
3.8 Summary
4 Multi-resolution feature extraction for speaker diarization
4.1 Introduction
4.2 Spectral analysis
4.2.1 Short-time Fourier transform
4.2.2 Multi-resolution time-frequency spectral analysis
4.3 Proposed analysis
4.4 Experimental setup
4.4.1 Database
4.4.2 Feature extraction
4.4.3 BK speaker modelling configuration
4.4.4 In-session speaker recognition
4.4.5 Speaker diarization experiment
4.5 Results
4.5.1 Speaker recognition
4.5.2 Speaker diarization
4.6 Summary
5 Speaker change detection with contextual information
5.1 Introduction and related work
5.2 The KBM as a context model
5.2.1 KBM composition methods
5.3 BK-based speaker change detection
5.4 Experimental setup
5.4.1 Database
5.4.2 Baseline SCD system
5.4.3 Binary key SCD system
5.4.4 Evaluation metrics
5.5 Results
5.5.1 SCD using cumulative vectors
5.5.2 SCD using binary keys
5.5.3 Comparison between BK-based SCD systems
5.5.4 Speaker diarization using a BK-based SCD
5.6 Summary
6 Leveraging spectral clustering for training-independent speaker diarization
6.1 Context and motivation
6.2 The first DIHARD challenge
6.3 An analysis of our baseline
6.3.1 The baseline system
6.3.2 Experiments and results
6.3.3 Identifying the baseline strengths & weaknesses
6.4 Spectral clustering
6.4.1 Introduction and motivation
6.4.2 Spectral clustering and BK speaker modelling
6.4.3 Single-speaker detection
6.5 Experimental setup
6.5.1 Dataset
6.5.2 Feature extraction
6.5.3 KBM and cumulative vector parameters
6.5.4 Clustering parameters
6.5.5 Evaluation
6.6 Results
6.6.1 Spectral clustering upon CVs
6.6.2 Spectral clustering as a number-of-speakers estimator
6.6.3 Evaluation of the single-speaker detector
6.6.4 Domain-based performance
6.6.5 Results in the official DIHARD classification
6.7 Summary
7 System combination
7.1 Motivation and context
7.2 Baseline system modules
7.2.1 Feature extraction
7.2.2 Voice activity detection and segmentation
7.2.3 Segment/cluster representation
7.2.4 Clustering
7.2.5 Resegmentation
7.3 Fusion
7.3.1 Fusion at similarity-matrix level
7.3.2 Fusion at the hypothesis level
7.4 Experimental setup
7.4.1 Training data
7.4.2 Development data
7.4.3 Modules configuration
7.5 Results
7.5.1 Closed-set condition
7.5.2 Open-set condition
7.5.3 Conclusions and results in the challenge
7.6 Summary
II Low-latency speaker spotting
8 Speaker diarization: integration within a real application
8.1 Introduction
8.2 Related work
8.3 Low-latency speaker spotting
8.3.1 Task definition
8.3.2 Absolute vs. speaker latency
8.3.3 Detection under variable or fixed latency
8.4 LLSS solutions
8.4.1 Online speaker diarization
8.4.2 Speaker detection
8.5 LLSS assessment
8.5.1 Database
8.5.2 Protocols
8.6 Experimental results
8.6.1 LLSS performance: fixed latency
8.6.2 LLSS performance: variable latency
8.6.3 Diarization influences
8.7 Summary
9 Selective cluster enrichment
9.1 Introduction
9.2 Selective cluster enrichment
9.3 Experimental work
9.3.1 General setup
9.3.2 Results
9.4 Summary
10 Conclusions
10.1 Summary
10.2 Directions for future research
Bibliography
