Speaker Diarization

20 minute read

“Who spoke when? The art of untangling voices.”

1. Introduction

Speaker Diarization is the task of partitioning an audio stream into homogeneous segments according to the speaker identity. In simple terms: “Who spoke when?”

Input: Audio recording (e.g., meeting, podcast, phone call). Output: Timeline with speaker labels.

Example:

[00:00 - 00:15] Speaker A: "Hello, how are you?"
[00:15 - 00:30] Speaker B: "I'm doing well, thanks!"
[00:30 - 00:45] Speaker A: "Great to hear."

Applications:

Meeting Transcription: Zoom, Google Meet, Microsoft Teams.
Call Center Analytics: Separate agent from customer.
Podcast Indexing: Identify different speakers for search.
Forensics: Identify speakers in surveillance recordings.

2. Problem Formulation

Challenges:

Unknown Number of Speakers: We don’t know how many people are speaking.
Overlapping Speech: Multiple people speaking simultaneously.
Variable Segment Length: Speakers may talk for 1 second or 10 minutes.
Acoustic Variability: Background noise, channel effects, emotion.

Metrics:

Diarization Error Rate (DER): Percentage of time that is incorrectly attributed.
- $DER = \frac{FA + MISS + CONF}{TOTAL}$
- FA (False Alarm): Non-speech detected as speech.
- MISS: Speech detected as non-speech.
- CONF (Confusion): Speech attributed to wrong speaker.

3. Traditional Pipeline

The classic diarization system has 4 stages:

3.1. Speech Activity Detection (SAD)

Goal: Remove silence and non-speech (music, noise).
Method: Energy-based VAD or DNN-based VAD.

3.2. Segmentation

Goal: Split audio into short, homogeneous segments (e.g., 1-2 seconds).
Method:
- Fixed-Length: Simple, but may split mid-sentence.
- Change Point Detection: Detect speaker changes using Bayesian Information Criterion (BIC) or GLR (Generalized Likelihood Ratio).

3.3. Embedding Extraction

Goal: Convert each segment into a fixed-size vector (embedding) that represents the speaker’s voice.
Method:
- i-vectors: Traditional approach using GMM-UBM.
- x-vectors: Deep learning approach using TDNN (Time-Delay Neural Network).
- d-vectors: Trained with triplet loss.

3.4. Clustering

Goal: Group segments by speaker.
Method:
- Agglomerative Hierarchical Clustering (AHC): Bottom-up clustering.
- Spectral Clustering: Graph-based clustering.
- PLDA (Probabilistic Linear Discriminant Analysis): Probabilistic scoring.

4. Deep Dive: X-Vectors

Architecture:

Input: Mel-Frequency Cepstral Coefficients (MFCCs) or Mel-Filterbanks.
Layers:
1. Frame-level TDNN: 5 layers with temporal context (e.g., [-2, +2] frames).
2. Statistics Pooling: Compute mean and standard deviation across time.
3. Segment-level Fully Connected: 2 layers.
4. Output: 512-dimensional embedding.

Training:

Dataset: VoxCeleb (1M+ utterances, 7000+ speakers).
Loss: Softmax (speaker classification) or AAM-Softmax (Additive Angular Margin).

Inference:

Extract x-vector for each segment.
Compute cosine similarity between x-vectors.
Cluster based on similarity.

5. Deep Dive: Clustering Algorithms

5.1. Agglomerative Hierarchical Clustering (AHC)

Algorithm:

Start with each segment as its own cluster.
Merge Step: Find the two closest clusters and merge them.
Repeat until a stopping criterion is met (e.g., threshold on distance, or target number of clusters).

Distance Metrics:

Average Linkage: Average distance between all pairs.
Complete Linkage: Maximum distance between any pair.
PLDA Score: Probabilistic score.

Stopping Criterion:

Threshold: Stop when the minimum distance > threshold.
Eigengap: Use spectral clustering to estimate the number of speakers.

5.2. Spectral Clustering

Algorithm:

Construct an Affinity Matrix $A$ where $A_{ij} = \text{similarity}(x_i, x_j)$.
Compute the Graph Laplacian $L = D - A$ where $D$ is the degree matrix.
Compute the eigenvectors of $L$.
Use the first $k$ eigenvectors as features.
Apply k-means clustering.

Advantage: Can handle non-convex clusters.

6. End-to-End Neural Diarization (EEND)

Idea: Train a single neural network to directly predict speaker labels for each frame.

Architecture:

Input: Mel-Filterbanks (e.g., 500 frames).
Encoder: Transformer or LSTM.
Output: $T \times K$ matrix where $T$ is time frames, $K$ is max number of speakers. Each entry is the probability that speaker $k$ is active at time $t$.

Training:

Loss: Binary Cross-Entropy for each speaker.
Permutation Invariant Training (PIT): Since speaker labels are arbitrary, we need to find the best permutation of predicted labels to match ground truth.

Advantage:

No need for separate segmentation and clustering.
Can handle overlapping speech naturally.

Disadvantage:

Requires large amounts of labeled data.
Fixed maximum number of speakers.

7. Handling Overlapping Speech

Traditional diarization assumes only one speaker at a time. In reality, people interrupt each other.

Solutions:

1. Multi-Label Classification:

Instead of assigning one speaker per frame, allow multiple speakers.
Output: $T \times K$ binary matrix.

2. Source Separation:

Use a speech separation model (e.g., Conv-TasNet) to separate overlapping speakers.
Then run diarization on each separated stream.

3. EEND with Overlap:

Train EEND to predict overlapping speakers.

8. System Design: Real-Time Diarization for Video Conferencing

Scenario: Zoom meeting with 10 participants. We want to display speaker labels in real-time.

Constraints:

Latency: < 1 second.
Accuracy: DER < 10%.
Scalability: Handle 100+ concurrent meetings.

Architecture:

Step 1: Audio Capture

Each participant’s audio is streamed to the server.

Step 2: VAD

Run a lightweight VAD model (e.g., WebRTC VAD) to detect speech.

Step 3: Embedding Extraction

Use a streaming x-vector model.
Extract embeddings every 1 second (with 0.5s overlap).

Step 4: Online Clustering

Use Online AHC or Online Spectral Clustering.
Update clusters incrementally as new embeddings arrive.

Step 5: Speaker Tracking

Maintain a “speaker profile” for each participant.
Match new embeddings to existing profiles.

Step 6: Display

Send speaker labels back to clients.
Display “Speaker A is talking” in the UI.

Optimization:

Batching: Process multiple meetings in parallel on GPU.
Caching: Cache embeddings for known speakers.

9. Deep Dive: PLDA (Probabilistic Linear Discriminant Analysis)

PLDA is a probabilistic model for speaker verification and diarization.

Model: $x = m + Fy + \epsilon$

$x$: Observed embedding (x-vector).
$m$: Global mean.
$F$: Factor loading matrix (speaker variability).
$y$: Latent speaker factor.
$\epsilon$: Residual noise.

Scoring: Given two embeddings $x_1$ and $x_2$, compute the log-likelihood ratio: $\text{score} = \log \frac{P(x_1, x_2 | \text{same speaker})}{P(x_1, x_2 | \text{different speakers})}$

Use in Diarization:

Use PLDA score as the distance metric in AHC.

10. Evaluation Metrics

Diarization Error Rate (DER): $DER = \frac{\text{False Alarm} + \text{Missed Speech} + \text{Speaker Confusion}}{\text{Total Speech Time}}$

Jaccard Error Rate (JER): Measures overlap between predicted and ground truth speaker segments.

Optimal Mapping: Since speaker labels are arbitrary (Speaker A vs Speaker 1), we need to find the optimal mapping between predicted and ground truth labels (Hungarian algorithm).

11. Datasets

1. CALLHOME:

Telephone conversations.
2-7 speakers per conversation.
Used in NIST evaluations.

2. AMI Meeting Corpus:

Meeting recordings.
4 speakers per meeting.
Includes video and slides.

3. DIHARD:

Diverse domains (meetings, interviews, broadcasts, YouTube).
Challenging: overlapping speech, noise.

4. VoxConverse:

YouTube videos.
Variable number of speakers.

12. Interview Questions

Explain Speaker Diarization. What are the main challenges?
X-Vectors vs I-Vectors. What are the differences?
Clustering. Why use AHC instead of k-means?
Overlapping Speech. How do you handle it?
Real-Time Diarization. How would you design a system for live meetings?
Calculate DER. Given ground truth and predictions, compute DER.

13. Common Mistakes

Ignoring Overlapping Speech: Assuming only one speaker at a time leads to high confusion errors.
Fixed Number of Speakers: Using k-means with a fixed $k$ when the number of speakers is unknown.
Poor VAD: If VAD misses speech or includes noise, diarization will fail.
Not Normalizing Embeddings: Cosine similarity requires normalized embeddings.
Overfitting to Domain: A model trained on telephone speech may fail on meeting recordings.

14. Advanced EEND Architectures

14.1. EEND-EDA (Encoder-Decoder Attractor)

Problem: Standard EEND has a fixed maximum number of speakers.

Solution: Use an encoder-decoder architecture with attractors.

Architecture:

Encoder: Transformer encodes the audio.
Attractor Estimation: A decoder estimates the number of speakers and their “attractors” (prototype embeddings).
Speaker Assignment: For each frame, compute similarity to each attractor. Assign to the closest attractor.

Benefit: Handles variable number of speakers without retraining.

14.2. EEND with Self-Attention

Idea: Use self-attention to model long-range dependencies between frames.

Architecture:

Input: Mel-Filterbanks.
Encoder: Multi-head self-attention (Transformer).
Output: Speaker activity matrix.

Training:

Permutation Invariant Training (PIT): Find the best permutation of predicted speakers to match ground truth.
Loss: Binary Cross-Entropy for each speaker.

Result: State-of-the-art performance on CALLHOME (DER < 10%).

14.3. EEND with Target-Speaker Voice Activity Detection (TS-VAD)

Idea: Given a target speaker’s enrollment audio, detect when that speaker is active.

Use Case: “Show me all segments where Alice spoke.”

Architecture:

Input: (1) Meeting audio, (2) Enrollment audio of Alice.
Encoder: Extract embeddings for both.
Attention: Cross-attention between meeting and enrollment.
Output: Binary mask (Alice active or not).

15. Production Case Study: Zoom Diarization

Scenario: Zoom meeting with 10 participants. Display speaker labels in real-time.

Challenges:

Latency: < 1 second.
Accuracy: DER < 10%.
Scalability: 100K+ concurrent meetings.

Solution:

Step 1: Audio Capture

Each participant’s audio is streamed to the server (separate tracks).

Step 2: VAD

Run WebRTC VAD on each track.

Step 3: Embedding Extraction

Use a streaming x-vector model (ResNet-based).
Extract embeddings every 1 second.

Step 4: Online Clustering

Use Online AHC with PLDA scoring.
Update clusters incrementally.

Step 5: Speaker Tracking

Maintain a “speaker profile” for each participant.
Use speaker verification to match new embeddings to profiles.

Step 6: Display

Send speaker labels to clients via WebSocket.

Optimization:

GPU Batching: Process 100 meetings in parallel on a single V100.
Caching: Cache embeddings for known speakers (reduces compute by 50%).

Result:

Latency: 500ms.
DER: 8%.
Cost: $0.01/hour/meeting.

Idea: Use visual cues (lip movement, face detection) to improve diarization.

Approach:

1. Face Detection:

Use MTCNN or RetinaFace to detect faces in each frame.

2. Active Speaker Detection (ASD):

Train a model to predict if a face is speaking based on lip movement.
Input: Face crop + audio.
Output: Probability of speaking.

3. Fusion:

Combine audio-based diarization with video-based ASD.
Rule: If audio says Speaker A is active AND face detection says Person 1 is speaking, then Person 1 = Speaker A.

Benefit: Reduces confusion errors by 30-50% in meetings with video.

17. Deep Dive: Online Diarization

Challenge: Traditional diarization is offline (requires the entire audio). For live meetings, we need online diarization.

Approach:

1. Sliding Window:

Process audio in chunks (e.g., 10 seconds).
Run diarization on each chunk.

2. Speaker Linking:

Link speakers across chunks using embeddings.
Challenge: Speaker labels may change across chunks (Speaker A in chunk 1 = Speaker B in chunk 2).
Solution: Use a global speaker tracker.

3. Incremental Clustering:

Use Online AHC or DBSCAN.
Add new segments to existing clusters or create new clusters.

18. Privacy & Security

Concerns:

Voice Biometrics: Speaker embeddings can be used to identify individuals.
Surveillance: Diarization enables tracking who said what.

Solutions:

1. On-Device Diarization:

Run diarization locally (no audio sent to cloud).
Challenge: Requires lightweight models.

2. Differential Privacy:

Add noise to embeddings to prevent re-identification.
Trade-off: Slight accuracy drop.

3. Anonymization:

Replace speaker labels with pseudonyms (Speaker 1, Speaker 2).
Don’t store raw audio, only transcripts.

19. Deep Dive: Speaker Change Detection

Problem: Detect when the speaker changes (boundary detection).

Approaches:

1. Bayesian Information Criterion (BIC):

For each potential change point $t$, compute:
- $BIC(t) = \log P(X_{1:t}) + \log P(X_{t+1:T}) - \log P(X_{1:T})$
If $BIC(t) > \theta$, there’s a speaker change at $t$.

2. Generalized Likelihood Ratio (GLR):

Similar to BIC, but uses likelihood ratio.

3. Neural Change Detection:

Train a CNN to predict speaker change points.
Input: Spectrogram.
Output: Binary label (change or no change).

20. Production Monitoring

Metrics to Track:

DER: Aggregate across all meetings. Alert if DER > 15%.
Latency: P95 latency. Alert if > 2 seconds.
Throughput: Meetings processed per second.
Error Analysis: Which types of errors are most common? (FA, MISS, CONF)

Dashboards:

Grafana: Real-time metrics.
Kibana: Log analysis (search for “speaker change detected”).

A/B Testing:

Deploy new model to 5% of meetings.
Compare DER with baseline.

21. Cost Analysis

Scenario: Diarization for 1M meetings/month (average 30 minutes each).

Baseline (Cloud-based):

Compute: 1M meetings × 30 min = 500K hours.
Cost: 500K hours × $0.10/hour (GPU) = $50K/month.

Optimized (Batching + Caching):

Batching: Process 100 meetings in parallel. Reduces GPU hours by 10x.
Caching: 40% cache hit rate. Reduces compute by 40%.
Cost: 500K hours × 0.1 (batching) × 0.6 (caching) × $0.10 = $3K/month.

Savings: $47K/month (94% cost reduction).

22. Advanced Technique: Few-Shot Speaker Diarization

Problem: Diarization fails when speakers have very short utterances (< 1 second).

Solution: Use few-shot learning.

Approach:

Meta-Learning: Train a model on many diarization tasks.
Prototypical Networks: Learn a metric space where speakers cluster tightly.
Inference: Given a few examples of each speaker, classify new segments.

Benefit: Works with as few as 3 seconds of enrollment audio per speaker.

23. Future Trends

1. Zero-Shot Diarization:

Diarize without any training data for the target domain.
Use pre-trained models (e.g., Wav2Vec 2.0) as feature extractors.

2. Continuous Learning:

Model adapts to new speakers over time.
Challenge: Catastrophic forgetting.

3. Multi-Lingual Diarization:

Handle meetings where speakers switch languages.
Challenge: Language-specific acoustic features.

4. Emotion-Aware Diarization:

Not just “who spoke” but “who spoke angrily/happily”.
Use Case: Call center analytics.

24. Benchmarking

Dataset: CALLHOME (2-speaker telephone conversations).

Method	DER (%)	Latency (s)	Model Size (MB)
i-vector + AHC	12.3	5.0	50
x-vector + AHC	9.8	3.0	20
x-vector + Spectral	8.5	4.0	20
EEND	7.2	2.0	100
EEND-EDA	6.5	2.5	120

Observation: EEND achieves the best DER but requires more compute.

25. Conclusion

Speaker diarization is a critical component of modern speech systems. From meeting transcription to call center analytics, the ability to answer “who spoke when” unlocks powerful applications.

Key Takeaways:

Traditional Pipeline: SAD → Segmentation → Embedding → Clustering.
EEND: End-to-end neural approach. Handles overlapping speech naturally.
X-Vectors: State-of-the-art embeddings for speaker recognition.
Clustering: AHC with PLDA scoring is the gold standard.
Production: Real-time diarization requires online clustering and caching.
Multi-Modal: Combining audio and video improves accuracy.

The future of diarization is multi-modal, privacy-preserving, and adaptive. As remote work becomes the norm, the demand for accurate, real-time diarization will only grow. Mastering these techniques is essential for speech engineers.

26. Deep Dive: ResNet-based X-Vector Architecture

Detailed Architecture:

Input: 40-dim MFCCs (T frames)
↓
Frame-level Layers:
  - TDNN1: 512 units, context [-2, +2]
  - TDNN2: 512 units, context [-2, 0, +2]
  - TDNN3: 512 units, context [-3, 0, +3]
  - TDNN4: 512 units, context {0}
  - TDNN5: 1500 units, context {0}
↓
Statistics Pooling: [mean, std] → 3000-dim
↓
Segment-level Layers:
  - FC1: 512 units
  - FC2: 512 units (x-vector embedding)
↓
Output: 7000 units (speaker classification)

Training Details:

Dataset: VoxCeleb1 + VoxCeleb2 (7000+ speakers, 1M+ utterances).
Augmentation: Add noise, reverb, codec distortion.
Loss: Softmax or AAM-Softmax (Additive Angular Margin).
Optimizer: Adam with learning rate 0.001.
Batch Size: 128 utterances.

Inference:

Extract the 512-dim embedding from FC2.
Normalize to unit length.
Use cosine similarity for clustering.

27. Deep Dive: VoxCeleb Dataset

VoxCeleb1:

Speakers: 1,251.
Utterances: 153K.
Duration: 352 hours.
Source: YouTube celebrity interviews.

VoxCeleb2:

Speakers: 6,112.
Utterances: 1.1M.
Duration: 2,442 hours.

Challenges:

In-the-Wild: Background noise, music, laughter.
Multi-Speaker: Some videos have multiple speakers.
Variable Length: Utterances range from 1 second to 10 minutes.

Preprocessing:

VAD: Remove silence using WebRTC VAD.
Segmentation: Split into 2-3 second chunks.
Normalization: Mean-variance normalization of MFCCs.

28. Production Optimization: Batch Processing

Problem: Processing 1M meetings sequentially takes days.

Solution: Batch processing on GPU.

Algorithm:

Collect 100 meetings.
Pad all audio to the same length (e.g., 30 minutes).
Extract MFCCs in parallel (GPU).
Batch Inference: Run x-vector model on all 100 meetings simultaneously.
Clustering: Run AHC on each meeting in parallel (CPU).

Speedup: 100x faster than sequential processing.

Implementation (PyTorch):

import torch

# Batch of 100 meetings, each 30 minutes (180K frames)
mfccs = torch.randn(100, 180000, 40).cuda()

# X-vector model
model = XVectorModel().cuda()
model.eval()

# Batch inference
with torch.no_grad():
    embeddings = model(mfccs)  # (100, T', 512)

# Post-process each meeting
for i in range(100):
    emb = embeddings[i]  # (T', 512)
    # Run clustering
    labels = cluster(emb)

29. Advanced Evaluation: Detailed Error Analysis

DER Breakdown:

False Alarm (FA): 2% (non-speech detected as speech).
Missed Speech (MISS): 3% (speech detected as non-speech).
Speaker Confusion (CONF): 5% (wrong speaker label).
Total DER: 10%.

Error Analysis:

FA: Mostly music and laughter.
- Fix: Improve VAD with music detection.
MISS: Mostly whispered speech.
- Fix: Train VAD on whispered speech data.
CONF: Mostly overlapping speech.
- Fix: Use EEND to handle overlaps.

30. Deep Dive: Permutation Invariant Training (PIT)

Problem: In EEND, speaker labels are arbitrary. Ground truth might be [A, B], but prediction might be [B, A].

Solution: PIT finds the best permutation.

Algorithm:

Predict: Model outputs $P \in \mathbb{R}^{T \times K}$ (K speakers).
Ground Truth: $Y \in \mathbb{R}^{T \times K}$.
Enumerate Permutations: For K=2, there are 2! = 2 permutations.
Compute Loss for Each Permutation:
- Perm 1: $L_1 = BCE(P[:, 0], Y[:, 0]) + BCE(P[:, 1], Y[:, 1])$
- Perm 2: $L_2 = BCE(P[:, 0], Y[:, 1]) + BCE(P[:, 1], Y[:, 0])$
Choose Minimum: $L = \min(L_1, L_2)$.

Complexity: $O(K!)$ for K speakers. For K>3, use Hungarian algorithm.

31. Production Case Study: Call Center Diarization

Scenario: Analyze 10K calls/day to separate agent from customer.

Challenges:

2-Speaker: Always agent + customer.
Overlapping Speech: Frequent interruptions.
Background Noise: Office noise, typing.

Solution:

Step 1: Stereo Audio

Agent and customer are on separate channels (stereo).
Benefit: No need for diarization! Just label left=agent, right=customer.

Step 2: Mono Audio (Fallback)

If stereo is unavailable, use diarization.
Optimization: Since K=2, use a simpler clustering algorithm (k-means with k=2).

Step 3: Speaker Verification

Verify that the agent is who they claim to be (security).
Extract x-vector from agent’s speech.
Compare with enrolled agent profile.

Result:

Latency: 10 seconds (for a 5-minute call).
DER: 5% (better than general diarization because K is known).

32. Advanced Technique: Self-Supervised Learning for Diarization

Problem: Labeled diarization data is expensive (need to manually annotate who spoke when).

Solution: Use self-supervised learning.

Approach:

Pretext Task: Train a model to predict if two segments are from the same speaker.
Contrastive Learning: Pull embeddings of the same speaker together, push different speakers apart.
Fine-Tuning: Fine-tune on a small labeled dataset.

Example: SimCLR for Speaker Embeddings:

# Positive pair: Two segments from the same speaker
emb1 = model(segment1)
emb2 = model(segment2)

# Negative pairs: Segments from different speakers
emb3 = model(segment3)

# Contrastive loss
loss = -log(exp(sim(emb1, emb2) / tau) / (exp(sim(emb1, emb2) / tau) + exp(sim(emb1, emb3) / tau)))

33. Interview Deep Dive: Diarization vs Speaker Recognition

Q: What’s the difference between speaker diarization and speaker recognition?

Diarization: “Who spoke when?” Unknown speakers. Clustering problem.
Recognition: “Is this speaker Alice?” Known speakers. Classification problem.

Q: Can you use the same model for both?

A: Yes! X-vectors can be used for both.

Diarization: Cluster x-vectors.
Recognition: Compare x-vector to enrolled speaker’s x-vector.

34. Future Trends: Transformer-based Diarization

EEND with Conformer:

Replace LSTM with Conformer (Convolution + Transformer).
Benefit: Better long-range dependencies.
Result: DER < 5% on CALLHOME.

Wav2Vec 2.0 for Diarization:

Use pre-trained Wav2Vec 2.0 as feature extractor.
Benefit: No need for MFCCs. Learn features end-to-end.
Challenge: Large model (300M params). Need compression for production.

35. Conclusion & Best Practices

Best Practices:

Start with X-Vectors + AHC: Proven, reliable, easy to implement.
Use PLDA Scoring: Better than cosine similarity for clustering.
Handle Overlapping Speech: Use EEND or multi-label classification.
Optimize for Production: Batch processing, caching, GPU acceleration.
Monitor DER: Track FA, MISS, CONF separately for targeted improvements.

Diarization Checklist:

The journey from “who spoke when” to production-ready diarization involves mastering embeddings, clustering, and system design. As meetings move online and voice interfaces proliferate, diarization will become even more critical. The techniques you’ve learned here—from x-vectors to EEND to multi-modal fusion—will serve you well in building the next generation of speech systems.

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1. Introduction

2. Problem Formulation

3. Traditional Pipeline

3.1. Speech Activity Detection (SAD)

3.2. Segmentation

3.3. Embedding Extraction

3.4. Clustering

4. Deep Dive: X-Vectors

5. Deep Dive: Clustering Algorithms

5.1. Agglomerative Hierarchical Clustering (AHC)

5.2. Spectral Clustering

6. End-to-End Neural Diarization (EEND)

7. Handling Overlapping Speech

8. System Design: Real-Time Diarization for Video Conferencing

9. Deep Dive: PLDA (Probabilistic Linear Discriminant Analysis)

10. Evaluation Metrics

11. Datasets

12. Interview Questions

13. Common Mistakes

14. Advanced EEND Architectures

14.1. EEND-EDA (Encoder-Decoder Attractor)

14.2. EEND with Self-Attention

14.3. EEND with Target-Speaker Voice Activity Detection (TS-VAD)

15. Production Case Study: Zoom Diarization

16. Multi-Modal Diarization (Audio + Video)

17. Deep Dive: Online Diarization

18. Privacy & Security

19. Deep Dive: Speaker Change Detection

20. Production Monitoring

21. Cost Analysis

22. Advanced Technique: Few-Shot Speaker Diarization

23. Future Trends

24. Benchmarking

25. Conclusion

26. Deep Dive: ResNet-based X-Vector Architecture

27. Deep Dive: VoxCeleb Dataset

28. Production Optimization: Batch Processing

29. Advanced Evaluation: Detailed Error Analysis

30. Deep Dive: Permutation Invariant Training (PIT)

31. Production Case Study: Call Center Diarization

32. Advanced Technique: Self-Supervised Learning for Diarization

33. Interview Deep Dive: Diarization vs Speaker Recognition

34. Future Trends: Transformer-based Diarization

35. Conclusion & Best Practices

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