What is speaker diarization and how does it work? (Complete 2026 Guide)
In this blog post, we'll take a closer look at how speaker diarization works, why it's useful, some of its current limitations, and how to easily use it on audio/video files.
With a recent market survey revealing that 76% of companies embed conversation intelligence in more than half of their customer interactions, understanding its core components is more important than ever. In this blog post, we'll take a closer look at how one of those components, speaker diarization, works, why it's useful, some of its current limitations, and how to easily use it on audio/video files.
What is speaker diarization?
Speaker diarization is an AI process that automatically identifies who spoke when in audio recordings containing multiple speakers. The technology detects distinct voices and assigns each word or segment to the correct speaker label (like "Speaker A" or "Speaker B"), transforming unstructured conversation audio into organized, speaker-attributed transcripts.
In Automatic Speech Recognition (ASR), this involves two key functions: detecting the number of unique speakers and attributing each word to its speaker.
Speaker diarization performs two key functions:
Speaker Detection: Identifying the number of distinct speakers in an audio file.
Speaker Attribution: Assigning segments of speech to the correct speaker.
The result is a transcript where each segment of speech is tagged with a speaker label (e.g., "Speaker A," "Speaker B"), making it easy to distinguish between different voices.
How does speaker diarization work?
Speaker diarization applies speaker labels ("Speaker A," "Speaker B") to each transcribed word. Modern AI models execute this through four core steps:
Step 1: Audio Segmentation
Step 2: Speaker Embedding Generation
Step 3: Speaker Count Estimation
Step 4: Clustering and Assignment
Step 1: Audio segmentation
Audio segmentation divides recordings into utterances of 0.5-10 seconds each. AI models need sufficient audio data to identify speakers accurately.
Example utterances:
Utterance 1: "Hello my name is Cindy"
Utterance 2: "I like dogs and live in San Francisco"
Each utterance gets assigned to a speaker label during the clustering process.
There are many ways to break up an audio/video file in a set of utterances, with one common way being to use silence and punctuation markers. In our research, we start seeing a drop off in the feature's ability to correctly assign an utterance to a speaker when utterances are less than one second.
Step 2: Speaker embedding generation
Each utterance passes through an AI model that generates speaker embeddings. As academic research shows, these embeddings—high-dimensional numerical representations of unique speaker characteristics—became a standard approach after i-vectors found great success in speaker recognition.
The visualization below shows how embeddings capture speaker features:
We do a similar process to convert not words, but segments of audio, into embeddings as well.
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Next, we need to make a choice about how many speakers are present in the audio file—this is a key feature of a modern speaker diarization model. Legacy Diarization systems required knowing how many speakers were in an audio/video file ahead of time, but a major benefit of modern models is that they can accurately predict this number.
Our first goal here is to overestimate the number of speakers. Through clustering methods, we want to estimate the highest number of speakers that is reasonably possible. Why overestimate? It's much easier to combine speakers' utterances if the model breaks them up into different speaker labels than it is to disentangle two speakers being combined into one.
After this initial step, we go back and combine speakers, or disentangle speakers, as needed to get an accurate number.
Step 4: Clustering and assignment
Finally, speaker diarization models take the embeddings (produced above), and cluster them into as many clusters as there are speakers. For example, if a diarization model predicts there are four speakers in an audio file, the embeddings will be forced into four groups based on the "similarity" of the embeddings.
For example, in the below image, let's assume each dot is an utterance. The utterances get clustered together based on their similarity—with the idea being that each cluster is a unique speaker.
There are many ways to determine similarity of embeddings, and this is a core component of accurately predicting speaker labels with a Speaker Diarization model. Recent architectural advances in speaker embedding models have improved clustering accuracy, particularly for short utterances and challenging acoustic conditions.
After this step, you now have a transcription complete with accurate speaker labels!
By default, AssemblyAI's models are optimized to identify up to 10 distinct speakers. For use cases with a known number of participants outside this range, you can use the speaker_options parameter to specify a minimum and maximum number of expected speakers.
Why is speaker diarization useful?
Speaker diarization is useful because it takes a big wall of text and breaks it into something much more meaningful and valuable. If you were to try and read a transcription without speaker labels, your brain would automatically try and assign each word/sentence to the appropriate speaker. It also saves you time and mental energy.
For example, let's look at the before and after transcripts below with and without speaker diarization:
Without:
But how did you guys first meet and how do you guys know each other? I actually met her not too long ago. I met her, I think last year in December, during pre season, we were both practicing at Carson a lot. And then we kind of met through other players. And then I saw her a few her last few torments this year, and we would just practice together sometimes, and she's really, really nice. I obviously already knew who she was because she was so good. Right. So. And I looked up to and I met her. I already knew who she was, but that was cool for me. And then I watch her play her last few events, and then I'm actually doing an exhibition for her charity next month. I think super cool. Yeah. I'm excited to be a part of that. Yeah. Well, we'll definitely highly promote that. Vania and I are both together on the Diversity and Inclusion committee for the USDA, so I'm sure she'll tell me all about that. And we're really excited to have you as a part of that tournament. So thank you so much. And you have had an exciting year so far. My goodness. Within your first WTI 1000 doubles tournament, the Italian Open.Congrats to that. That's huge. Thank you.
With:
Speaker A: But how did you guys first meet and how do you guys know each other?
Speaker B: I actually met her not too long ago. I met her, I think last year in December, during pre season, we were both practicing at Carson a lot. And then we kind of met through other players. And then I saw her a few her last few torments this year, and we would just practice together sometimes, and she's really, really nice. I obviously already knew who she was because she was so good.
Speaker A: Right. So.
Speaker B: And I looked up to and I met her. I already knew who she was, but that was cool for me. And then I watch her play her last few events, and then I'm actually doing an exhibition for her charity next month.
Speaker A: I think super cool.
Speaker B: Yeah. I'm excited to be a part of that.
Speaker A: Yeah. Well, we'll definitely highly promote that. Vania and I are both together on the Diversity and Inclusion committee for the USDA. So I'm sure she'll tell me all about that. And we're really excited to have you as a part of that tournament. So thank you so much. And you have had an exciting year so far. My goodness. Within your first WTI 1000 doubles tournament, the Italian Open. Congrats to that. That's huge.
Speaker B: Thank you.
See how much easier the transcription is to read with speaker diarization?
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It is also a powerful analytic tool. In fact, industry research shows that analytics and intelligence are now the most common use cases for speech understanding. When you identify and label speakers, you can analyze each speaker's behaviors, identify patterns/trends among individual speakers, make predictions, and more. For example:
A call center might analyze agent messages versus customer requests, or complaints, to identify trends that could help facilitate better communication. This provides a significant advantage over manual methods, as historical data shows that QA teams could traditionally only review 2 to 4 calls per agent each month.
A podcast service might use speaker labels to identify the host and guest, making transcriptions more readable for end users.
A telemedicine platform might identify doctor and patient to create an accurate transcript, attach a readable transcript to patient files, or input the transcript into an EHR system. This is a critical function, as clinical studies recommend fine-tuning systems for different speaker roles to better handle the complex dialogue between doctors and patients.
Main approaches to speaker diarization
Speaker diarization systems use two fundamentally different technical approaches to solve the "who spoke when" problem. Understanding these methodologies is crucial for selecting the right solution and setting realistic performance expectations.
Pipeline-based (clustering) systems
The traditional approach treats speaker diarization as a multi-stage pipeline, where each component handles a specific task in sequence:
Voice Activity Detection (VAD): First identifies which parts of the audio contain speech versus silence or background noise
Segmentation: Divides the speech regions into uniform chunks for processing
Disadvantages: Error propagation—mistakes in early stages cascade through the entire pipeline
End-to-end neural systems
Modern end-to-end systems use a single neural network to perform speaker diarization directly. These systems, often built on transformer architectures, learn to map raw audio to speaker-labeled segments without explicit intermediate stages.
Rather than separate models for each task, one unified model learns the entire process. This approach can capture complex patterns that pipeline systems might miss. Recent architectural advances in end-to-end models allow them to better handle subtle voice changes, overlapping speech patterns, and brief utterances that challenged older systems. The trade-off is less interpretability; when something goes wrong, it's harder to diagnose which part of the system needs adjustment.
Hybrid approaches
Some systems combine elements of both approaches. They might use neural networks for embedding extraction but traditional clustering for speaker grouping, or employ end-to-end models for initial predictions followed by rule-based post-processing for refinement.
Handles complex patterns, fewer components to maintain
Less interpretable, requires more training data
Hybrid
Balances accuracy with interpretability
More complex architecture to implement
The choice between approaches often depends on your specific requirements. Pipeline systems work well when you need to understand and control each processing stage. End-to-end systems excel when you prioritize accuracy and have diverse, challenging audio conditions.
Evaluating speaker diarization performance
Diarization Error Rate (DER) measures accuracy as the percentage of incorrectly attributed speech time. This metric is part of a widely used evaluation scheme that has been a staple in diarization studies since the 2006 Rich Transcription (RT) evaluation.
DER calculation includes three error types:
Speaker confusion: The duration of speech that is assigned to the wrong speaker.
False alarm speech: The duration where non-speech audio (like silence or background noise) is incorrectly labeled as speech.
Missed detection: The duration of speech that the system fails to detect entirely.
The total error duration is then divided by the total duration of the audio file to get the final DER percentage. A lower DER indicates higher accuracy. Understanding this metric is key to benchmarking different models and choosing a solution that meets your application's accuracy requirements.
When benchmarking diarization systems, you'll also encounter the Speaker Count Error Rate metric, which measures accuracy in determining the correct number of speakers. AssemblyAI achieves a 2.9% speaker count error rate, providing reliable speaker identification even in challenging acoustic conditions.
Comparing speaker diarization solutions
Speaker diarization implementation involves choosing between two primary paths: open-source libraries or commercial APIs. This decision impacts development time, accuracy, maintenance requirements, and total cost of ownership.
Open-source libraries
Open-source diarization tools provide complete control over the implementation. Libraries like pyannote.audio and NVIDIA NeMo offer state-of-the-art models you can customize for specific use cases.
Benefits of open-source:
Full customization of model architectures and parameters
No per-usage costs for processing
Complete data privacy—audio never leaves your infrastructure
Ability to fine-tune models on domain-specific data
Challenges to consider:
Significant engineering effort for deployment and maintenance
Infrastructure costs for GPU servers and scaling
Ongoing work to incorporate latest research improvements
Complex optimization for production-level accuracy
Commercial Voice AI APIs
API-based solutions like AssemblyAI provide production-ready speaker diarization through simple API calls. These services handle the complexity of model optimization, infrastructure scaling, and continuous improvements.
Benefits of commercial APIs:
Immediate access to state-of-the-art models
No infrastructure or maintenance overhead
Automatic improvements as new models are released
Enterprise-grade reliability and support
Considerations for APIs:
Usage-based pricing model
Less customization flexibility
Audio data processed on provider's infrastructure, which is a key consideration, as a recent industry survey found that over 30% of product leaders cite data privacy and security as a significant challenge.
Making the right choice
Decision framework: Use these criteria to evaluate which approach matches your specific requirements:
Factor
Choose Open-Source When
Choose API When
Timeline
You have months for implementation
You need to ship quickly
Team expertise
Deep ML and audio processing knowledge
Focus on application development
Customization
Unique requirements need model modifications
Standard diarization meets your needs
Scale
Predictable, high-volume processing
Variable or growing usage patterns
Budget
High upfront investment acceptable
Prefer operational expenses
Many successful products started with APIs to validate their use case, then evaluated building in-house once they understood their specific requirements and scale. This approach minimizes initial investment while maximizing learning.
These applications are already delivering measurable results for organizations across industries. For example, hiring intelligence platform Screenloop uses AI-powered transcription and speaker diarization to help its customers realize a 90% reduction in time spent on manual hiring and interview tasks, 20% reduced time to hire, and improved training effectiveness while reducing hiring bias.
Limitations and challenges of speaker diarization
Currently, Speaker Diarization is primarily designed for asynchronous (pre-recorded) transcription to ensure the highest accuracy. While direct real-time diarization on a single audio stream is not a standard feature, it can be achieved by using multichannel audio, where each speaker is on a separate channel. According to a recent survey, over 80% of product leaders predict real-time capabilities will be the most transformative development in speech understanding.
There are also several constraints that limit the accuracy of modern models:
Speaker talk time
Conversational pace
Minimum speaker requirements:
15+ seconds: Unreliable detection, may assign as unknown
30+ seconds: Reliable speaker identification
<15 seconds: Often merged with dominant speakers
Conversational pace significantly impacts diarization accuracy. Well-structured conversations work best for accurate speaker identification:
Energetic conversations with crosstalk reduce model accuracy compared to structured dialogues. In fact, research findings confirm that error rates can rise to over 50% in conversational, multi-speaker scenarios compared to controlled dictation settings.
If overtalk (aka crosstalk) is common, the model may struggle. As this guide explains, less advanced systems might merge speakers or miss the overlapping speech, while others may even misidentify an imaginary third speaker, which includes the portions of overtalk.
Different providers have varying speaker limits. AssemblyAI supports up to 10 speakers by default, but you can use the speaker_options parameter to specify a different range for your specific use case, though accuracy may decrease with a higher number of speakers.
Recent improvements have reduced errors when speakers have similar voices.
While there are clearly some limitations to speaker diarization today, Speech-to-Text APIs like AssemblyAI are using deep learning research to overcome these deficiencies. These efforts are leading to documented improvements, with some models showing a 30% boost in accuracy in noisy environments, helping to boost overall speaker diarization performance.
AssemblyAI's new speaker diarization model delivers significant improvements in real-world audio conditions:
20.4% error rate in noisy, far-field scenarios (down from 29.1%) - a 30% improvement for challenging acoustic environments where traditional systems fail
Accurate speaker identification for 250ms segments - enabling tracking of single words and brief acknowledgments
57% improvement in mid-length reverberant audio - better performance in conference rooms and large spaces
Automatic deployment - All customers benefit immediately with no code changes required
These improvements specifically target the challenging scenarios that break existing systems: conference room recordings with ambient noise, multi-speaker discussions with overlapping voices, and remote meetings with poor audio quality. Learn more about implementation options.
Getting started with speaker diarization
Speaker diarization transforms unstructured audio into organized, analyzable data. By accurately identifying who said what, you can power more intelligent features in your applications, from meeting summaries to call center analytics.
The best way to understand the impact of diarization is to test it on your own audio files. You can start building for free with our API to see how our models perform on the real-world audio your application will handle. Try our API for free to get your API key and run your first diarization request in minutes.
Frequently asked questions about speaker diarization
What's the difference between speaker diarization and speaker recognition?
Speaker diarization labels unknown voices as "Speaker A/B," while speaker recognition identifies specific individuals from voice databases.
How many speakers can speaker diarization detect?
Most production systems handle 2-10 speakers reliably, with some supporting up to 30+ speakers depending on audio quality and provider capabilities.
Which languages support speaker diarization?
AssemblyAI's Universal-2 model supports speaker diarization across 99 languages. For the highest accuracy, our Universal-3-Pro model supports speaker diarization for English, Spanish, Portuguese, French, German, and Italian. This broad support makes it possible to analyze conversations in a global context.
Can speaker diarization work in real-time?
Most systems process audio asynchronously for best accuracy, though real-time diarization is emerging for live applications.
How accurate is speaker diarization?
Accuracy is measured by Diarization Error Rate (DER), where lower is better. Leading systems like AssemblyAI achieve low DERs on challenging datasets. For example, our latest models show a 30% relative improvement in noisy environments, enabling reliable speaker tracking even for single words or brief acknowledgments.
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