November 18, 2025

Using multichannel and speaker diarization

Learn how multichannel transcription and speaker diarization work, what their outputs look like, when to use each feature, and how you can implement them.

Tutorial

Speaker Diarization

Multichannel

Patrick Loeber

Senior Developer Advocate

Patrick Loeber

Senior Developer Advocate

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Separating and identifying multiple speakers in audio recordings is essential for creating accurate, organized transcripts, and it's a capability gaining significant attention; in fact, an industry survey found that 50% of respondents are excited about the future of speaker recognition and voice embeddings. Two techniques that make this possible are multichannel transcription and speaker diarization.

Multichannel transcription, also known as channel diarization, processes audio with separate channels for each speaker, making it easier to isolate individual contributions. Speaker diarization distinguishes speakers in single-channel recordings. Both methods help create structured transcripts that are easy to analyze and use.

In this article, we'll explain how multichannel transcription and speaker diarization work, what their outputs look like, and how you can implement them using AssemblyAI's Voice AI models.

Understanding multichannel transcription

Multichannel transcription separates speakers by processing distinct audio channels—each channel contains one speaker's audio. This eliminates speaker overlap and delivers higher accuracy than single-channel processing.

Common multichannel setups include:

Conference calls: Each participant's microphone records to a separate channel
Stereo recordings: Interviewer on left channel, interviewee on right channel
Podcast recordings: Hosts and guests on individual channels
Customer service: Agent and customer on separate channels

Multichannel transcription delivers higher accuracy because speakers are pre-separated at the recording level.

Note: Multichannel audio increases transcription time by approximately 25%.

Understanding speaker diarization

Speaker diarization identifies and distinguishes speakers within single-channel audio recordings. It answers the question: "Who spoke when?" by segmenting the audio into speaker-specific portions.

Speaker diarization works on single-channel audio by analyzing voice characteristics like pitch, tone, and cadence. It identifies "who spoke when" even with overlapping speech.

This technique is especially valuable in scenarios like recorded meetings, interviews, and panel discussions where speakers share the same recording track, with documented applications in fields ranging from market research and healthcare to call centers and legal depositions. For instance, a single-channel recording of a business meeting can be processed with diarization to label each participant's speech, providing a structured transcript that makes conversations easy to follow.

Important: Speaker Diarization and Multichannel transcription cannot be used together. Enabling both features will result in an error. Choose the method that best fits your audio recording setup.

By using speaker diarization, you can create clear and organized transcripts without the need for separate audio channels. This ensures accurate speaker attribution, improves usability, and allows for deeper insights into speaker-specific contributions in any audio recording.

Choosing between multichannel and speaker diarization

Deciding between Multichannel transcription and Speaker Diarization depends on the structure of your audio and your specific needs. Both approaches are effective for separating and identifying speakers, but they are suited to different scenarios.

When to use multichannel transcription

Multichannel transcription is ideal when your recording setup allows for distinct audio channels for each speaker or source. For example, conference calls, podcast recordings, or customer service calls often produce multichannel audio files. With each speaker recorded on a separate channel, transcription becomes straightforward, as there's no need to differentiate speakers within a single track. Multichannel transcription ensures clarity, reduces overlap issues, and is particularly useful when high accuracy is required.

When to use speaker diarization

Speaker diarization is the better choice for single-channel recordings where all speakers share the same audio track. This technique is commonly applied in scenarios like in-person interviews, panel discussions, or courtroom recordings. Diarization uses advanced algorithms to differentiate speakers, making it effective when you don't have the option to record each participant on their own channel.

Making the right choice

If your recording setup supports separate channels for each speaker, multichannel transcription is generally the more precise and efficient option.

However, if your audio is limited to a single channel or includes overlapping voices, speaker diarization is essential for creating structured and accurate transcripts.

Ultimately, the choice depends on the recording setup and the level of detail needed for the transcript.

How to implement multichannel transcription with AssemblyAI

You can use the API or one of the AssemblyAI SDKs to implement multichannel transcription (see developer documentation).

Let's see how to use multichannel transcription with the AssemblyAI Python SDK:

import assemblyai as aai audio_file = "./multichannel-example.mp3" config = aai.TranscriptionConfig(multichannel=True) transcript = aai.Transcriber().transcribe(audio_file, config) print(f"Number of audio channels: {transcript.json_response['audio_channels']}") for utt in transcript.utterances: print(f"Speaker {utt.speaker}, Channel {utt.channel}: {utt.text}")

Set multichannel=True in your TranscriptionConfig and call transcribe():

The response includes speaker, channel, and text for each utterance.

Multichannel response

With AssemblyAI, you can transcribe each audio channel independently by configuring the multichannel parameter. See how to implement it in the previous section.

Here is an example JSON response for an audio file with two separate channels when multichannel transcription is enabled:

{ "multichannel": true, "audio_channels": 2, "utterances": [ { "text": "Here is Laura talking on channel one.", "speaker": "1", "channel": "1", "start": 1000, "end": 4500, "confidence": 0.98, "words": [ { "text": "Here", "speaker": "1", "channel": "1", "start": 1000, "end": 1350, "confidence": 0.99 }, { "text": "is", "speaker": "1", "channel": "1", "start": 1400, "end": 1550, "confidence": 0.98 } ] }, { "text": "And here is Alex talking on channel two.", "speaker": "2", "channel": "2", "start": 5000, "end": 8200, "confidence": 0.97, "words": [ { "text": "And", "speaker": "2", "channel": "2", "start": 5000, "end": 5250, "confidence": 0.99 }, { "text": "here", "speaker": "2", "channel": "2", "start": 5300, "end": 5600, "confidence": 0.98 } ] } ] }

The response contains the multichannel field set to true, and the audio_channels field with the number of different channels.

The important part is in the utterances field. This field contains an array of individual speech segments, each containing the details of one continuous utterance from a speaker. For each utterance, a unique identifier for the speaker (e.g., 1, 2) and the channel number are provided.

Additionally, the words field is provided, containing an array of information about each word, again with speaker and channel information.

Build with multichannel transcription

Get an API key to transcribe separate audio channels and return speaker and channel metadata with the SDK or REST API.

How to implement speaker diarization with AssemblyAI

Speaker diarization is also supported through the API or one of the AssemblyAI SDKs (see developer documentation).

Here's how to implement speaker diarization with the Python SDK:

import assemblyai as aai audio_file = "./monochannel-example.mp3" # Basic diarization config = aai.TranscriptionConfig(speaker_labels=True) # Or, provide the exact number of speakers # config = aai.TranscriptionConfig(speaker_labels=True, speakers_expected=2) # Or, provide a range of expected speakers # config = aai.TranscriptionConfig( # speaker_labels=True, # speaker_options=aai.SpeakerOptions( # min_speakers_expected=2, # max_speakers_expected=5, # ) # ) transcript = aai.Transcriber().transcribe(audio_file, config) for utt in transcript.utterances: print(f"Speaker {utt.speaker}: {utt.text}")

To enable speaker diarization in the Python SDK, set speaker_labels=True in your TranscriptionConfig. You can improve diarization performance by providing the number of speakers. If you know the exact number, set the speakers_expected parameter. If you have an estimated range, use speaker_options to set min_speakers_expected and max_speakers_expected for more flexible and accurate results.

Then, create a Transcriber object and call the transcribe function with the audio file and the configuration. When the transcription process is finished, we can again iterate over the separate utterances while accessing the speaker label and the text of each utterance.

The code is similar to the Multichannel code example except for enabling speaker_labels instead of multichannel, and the result does not contain audio_channels and channel information.

Speaker diarization response

The API also supports speaker diarization by configuring the speaker_labels parameter. You'll see how to implement it in the previous section.

Here is an example JSON response for a monochannel audio file when speaker_labels is enabled:

{ "multichannel": null, "audio_channels": null, "utterances": [ { "text": "Today, I'm joined by Alex. Welcome, Alex!", "speaker": "A", "channel": null, "start": 1000, "end": 4500, "confidence": 0.98, "words": [ { "text": "Today", "speaker": "A", "channel": null, "start": 1000, "end": 1400, "confidence": 0.99 }, { "text": "I'm", "speaker": "A", "channel": null, "start": 1450, "end": 1650, "confidence": 0.97 } ] }, { "text": "I'm excited to be here!", "speaker": "B", "channel": null, "start": 5000, "end": 7200, "confidence": 0.96, "words": [ { "text": "I'm", "speaker": "B", "channel": null, "start": 5000, "end": 5200, "confidence": 0.98 }, { "text": "excited", "speaker": "B", "channel": null, "start": 5250, "end": 5750, "confidence": 0.95 } ] } ] }

The response is similar to a multichannel response, with an utterances and a words field including a speaker label (e.g. "A", "B").

The difference to a multichannel transcription response is that the speaker labels are denoted by "A", "B" etc. rather than numbers, and that the multichannel, the audio_channels, and the channel fields don't contain values.

How speaker diarization works

Speaker diarization identifies speakers in single-channel audio by analyzing voice characteristics and organizing speech into distinct segments. For developers working with multi-speaker audio, understanding this process helps optimize implementation and troubleshoot results.

The process involves three key steps that work together to separate speakers:

Segmentation: The audio is divided into time-based segments based on acoustic changes like pauses, tone shifts, or pitch variations. This creates boundaries where one speaker stops and another begins.
Voice analysis: Each segment is processed to extract unique voice characteristics including pitch, timbre, and vocal texture. These characteristics create a numerical "voice fingerprint" for each speaker, and thanks to recent breakthroughs in deep learning, this is typically done using neural network-based audio embeddings known as d-vectors.
Speaker grouping: Similar voice fingerprints are grouped together using clustering algorithms. Each group represents a distinct speaker, allowing the system to label speech segments accordingly.

This approach enables accurate speaker identification even when voices overlap or change throughout the recording, making it effective for real-world audio scenarios.

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Performance considerations and optimization

Performance varies by method:

Multichannel: Higher accuracy, 25% longer processing time.
Speaker diarization: Quality-dependent, requires clear audio with minimal overlap, though recent model improvements have achieved 30% better performance in noisy audio and overlapping conditions.
Optimization tip: For better diarization accuracy, provide the model with the number of speakers. Set the speakers_expected parameter if you know the exact number, or use speaker_options to provide a minimum and maximum range (e.g., min_speakers_expected=2, max_speakers_expected=5).

Debugging common implementation issues

When implementing speaker identification, you might run into a few common issues. Here's how to troubleshoot them:

Speakers not being separated: If you're using speaker diarization on a single-channel file and the speakers aren't being labeled, double-check that speaker_labels=True is set in your TranscriptionConfig.
Multichannel returns a single speaker: If you're using multichannel transcription but only getting one speaker back, verify that your audio file is a true multichannel file (like a stereo WAV) and not just a mono file saved in a stereo format.
API Error: If you receive an error, remember that multichannel and speaker_labels cannot be used together. You must choose one method per API request based on your audio source.

Start building with advanced speaker identification

Both multichannel transcription and speaker diarization are powerful techniques for creating structured, usable transcripts from multi-speaker audio. The key is choosing the right approach for your audio source.

With AssemblyAI, you can switch between them with a single parameter, giving you the flexibility to handle any audio format. Now that you have the code and concepts, you're ready to build more advanced Voice AI features. Try our API for free to get started.

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Frequently asked questions about multichannel speaker diarization

What is the difference between speaker segmentation and diarization?

Speaker segmentation identifies when speakers change, while diarization identifies both when and who is speaking.

Can I use multichannel transcription and speaker diarization together?

No, these features are mutually exclusive and will return an error if both are enabled.

How can I improve speaker diarization accuracy?

Use high-quality audio with minimal background noise. For better accuracy, provide the number of speakers by setting either the speakers_expected parameter (for an exact count) or the speaker_options parameter (for a range of speakers), which is critical because industry analysis shows that accuracy defines the quality of all downstream tasks like summaries, insights, and compliance.