What is Speaker Diarization?
Speaker Diarization is an AI technology that automatically identifies and segments audio recordings by speaker, answering the question "who spoke when." It analyses voice characteristics to distinguish between different speakers in a conversation, enabling structured transcripts for meetings, calls, and interviews.
What is Speaker Diarization?
Speaker Diarization is the process of partitioning an audio recording into segments based on who is speaking. The term "diarization" comes from "diary" — the system creates a diary of who spoke at each moment in the recording. When you see a meeting transcript that labels each statement with a speaker name or identifier, diarization is the technology that made it possible.
While speech recognition converts audio to text (what was said), and speaker recognition identifies who a voice belongs to, speaker diarization tackles a different challenge: determining when each speaker starts and stops talking in a multi-speaker recording, even when the system does not know the speakers' identities in advance.
How Speaker Diarization Works
Modern speaker diarization systems typically follow a pipeline of steps:
- Voice activity detection (VAD): First, the system identifies which segments of the audio contain speech versus silence, music, or background noise
- Speaker embedding extraction: For each speech segment, the system creates a compact mathematical representation (embedding) that captures the voice characteristics of whoever is speaking
- Clustering: The system groups segments with similar voice embeddings together, determining that segments sharing vocal characteristics likely come from the same speaker
- Labelling: Each cluster is assigned a speaker label (typically "Speaker 1," "Speaker 2," etc., unless matched against known voiceprints)
- Overlap detection: Advanced systems also identify moments where multiple speakers talk simultaneously, which is common in natural conversation
Offline vs Online Diarization
- Offline diarization processes a complete recording after it is finished, allowing the algorithm to use information from the entire recording for maximum accuracy. Best for meeting transcription and call centre analytics.
- Online (real-time) diarization processes audio as it streams, making speaker assignments in real time. More challenging technically but necessary for live captioning and real-time analytics applications.
Business Applications of Speaker Diarization
Meeting Intelligence
- Creating structured meeting transcripts that attribute statements to specific participants, making them searchable and actionable
- Tracking speaking time distribution to analyse meeting dynamics, identify who dominates discussions, and ensure inclusive participation
- Enabling automated meeting minutes that capture decisions and action items by speaker
- Building searchable archives where you can find "what did the CFO say about Q3 projections" across all recorded meetings
Contact Centre Analytics
- Separating agent and customer speech in call recordings for targeted quality analysis
- Measuring talk-time ratios between agents and customers, a key metric for customer service quality
- Identifying moments where customers and agents talk over each other, which often correlates with frustration or miscommunication
- Enabling speaker-specific sentiment analysis to track customer emotion separately from agent tone
Legal and Compliance
- Creating certified transcripts of depositions, hearings, and legal proceedings with reliable speaker attribution
- Monitoring recorded communications in financial services to ensure compliance with regulatory requirements
- Providing evidence-grade speaker attribution for recorded conversations in dispute resolution
Media and Content Production
- Automatically segmenting interviews and panel discussions by speaker for editing and indexing
- Creating speaker-attributed subtitles for video content
- Enabling automated highlight extraction based on specific speaker segments
Healthcare
- Transcribing multi-party clinical consultations with proper attribution to doctor, patient, and specialists
- Creating accurate records of medical team handoff conversations
- Documenting therapy sessions with speaker-separated notes
Speaker Diarization in Southeast Asia
Speaker diarization faces interesting challenges and opportunities in the ASEAN context:
- Multilingual meetings: Business meetings in Southeast Asia frequently involve participants speaking different languages. A meeting might feature speakers in English, Mandarin, and Malay. Diarization must handle language-switching between speakers without confusing language changes with speaker changes.
- Large meeting cultures: Business cultures in several ASEAN markets involve larger meeting groups than typical Western settings, sometimes with 10-20 participants. Accurate diarization with many speakers is significantly more challenging than for two-party conversations.
- Contact centre growth: Southeast Asia's business process outsourcing industry, particularly in the Philippines and Malaysia, handles millions of customer calls daily. Speaker diarization enables analysis of these interactions at scale, driving quality improvements and training insights.
- Regulatory compliance: As financial regulators across ASEAN strengthen requirements for communication monitoring and record-keeping, speaker diarization becomes essential for maintaining compliant, searchable records.
Common Misconceptions
"Speaker diarization can identify speakers by name." Standard diarization only labels speakers as "Speaker 1," "Speaker 2," and so on. Identifying speakers by name requires either integration with speaker recognition technology or meeting metadata that maps participants to voice profiles.
"Diarization handles overlapping speech perfectly." Overlapping speech, where two or more people talk at the same time, remains one of the most challenging aspects of diarization. Modern systems can detect overlap but assigning the correct text to each overlapping speaker is still an active area of research.
"More speakers means proportionally more errors." Diarization accuracy does degrade with more speakers, but not linearly. The bigger challenge is distinguishing between speakers with similar voice characteristics, such as two people of the same gender and age group, regardless of the total number of speakers.
Getting Started with Speaker Diarization
- Define your accuracy requirements based on the use case. Contact centre agent-customer separation needs high accuracy, while meeting notes may tolerate more errors.
- Evaluate cloud services from providers like Google Cloud Speech-to-Text, AWS Transcribe, and Assembly AI that include built-in diarization
- Test with representative audio from your actual environment, including your typical number of speakers and recording conditions
- Plan for post-processing to correct diarization errors, particularly for high-stakes applications like legal transcription
- Consider combining diarization with speaker recognition for known participants to improve accuracy and enable named speaker attribution
Speaker Diarization transforms unstructured audio into structured, attributable data that businesses can analyse, search, and act upon. Without diarization, a meeting recording or call transcript is a monolithic block of text. With diarization, it becomes an organised conversation where each statement is linked to a specific speaker, enabling analysis that was previously impossible or prohibitively expensive.
For CEOs, the value is in organisational intelligence. Diarized meeting transcripts create a searchable record of who said what, when, and in what context. This enables better accountability, decision tracking, and knowledge management across the organisation. In customer-facing operations, diarization separates agent and customer speech, enabling targeted quality monitoring and coaching.
For CTOs, diarization is a critical component in the speech analytics stack. It is the technology that makes downstream analysis meaningful — sentiment analysis by speaker, speaking time metrics, interruption detection, and topic attribution all depend on accurate diarization. In Southeast Asia's large and growing contact centre industry, diarization enables analytics at a scale that would be impossible with manual review. Companies processing thousands of calls daily can gain systematic insights into customer experience, agent performance, and operational trends, transforming voice data from a compliance archive into a strategic asset.
- Set realistic accuracy expectations. Even the best diarization systems achieve 85-95% accuracy for clear, two-speaker recordings. Accuracy decreases with more speakers, overlapping speech, and poor audio quality.
- Choose between offline and real-time diarization based on your use case. Offline processing is significantly more accurate but only suitable for post-hoc analysis, not live applications.
- Provide the expected number of speakers when possible. Many diarization systems perform better when told how many speakers to expect rather than estimating this automatically.
- Invest in good audio capture. Using individual microphones or multi-channel recording for meetings dramatically improves diarization accuracy compared to a single room microphone.
- Plan for speaker identity assignment. Diarization outputs generic labels like "Speaker 1." If you need named speakers, integrate with speaker recognition or use meeting metadata to map labels to identities.
- Test with your actual meeting and call dynamics, including the typical number of participants, language mix, and tendency toward overlapping speech in your organisation.
Frequently Asked Questions
How accurate is speaker diarization for business meetings?
For standard two-party conversations with clear audio, modern diarization systems achieve 90-95% accuracy in correctly attributing speech to the right speaker. For meetings with 3-6 participants, accuracy typically ranges from 85-92%. Larger meetings with 8+ participants may see accuracy drop to 75-85%, particularly when speakers have similar voice characteristics or when there is significant overlapping speech. Using individual microphones rather than a single room microphone can improve accuracy by 10-15 percentage points.
Can speaker diarization work in real time for live meetings?
Yes, real-time diarization is available from several providers including Google Cloud and Assembly AI. However, real-time diarization is generally less accurate than offline processing because the algorithm cannot look ahead in the audio stream. Real-time systems typically achieve 80-90% of the accuracy of their offline equivalents. For applications like live meeting captions with speaker labels, this is usually acceptable. For applications requiring high accuracy like compliance recording, offline processing of the complete recording is recommended.
More Questions
Speaker diarization and speaker recognition solve different problems. Diarization answers "who spoke when" by segmenting audio into speaker-labelled chunks, assigning generic labels like "Speaker 1" and "Speaker 2" without knowing who those speakers are. Speaker recognition answers "is this speaker person X?" by matching a voice against known voiceprints. In practice, the two are often combined: diarization segments the audio by speaker, and then speaker recognition identifies each speaker by matching their voice against a database of known individuals.
Need help implementing Speaker Diarization?
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