Most production audio is mono. Not because engineers prefer it, but because that's what the infrastructure delivers: telephony mixes, Zoom cloud recordings, browser-captured audio, podcast feeds, voicemail archives. When you record a call through a SIP trunk or capture a meeting via the cloud, you typically get a single audio stream where every speaker's voice is blended on one channel.

This creates an immediate problem for any Voice AI pipeline that needs to do something useful with that audio.

The wall-of-text problem

Run raw mono audio through a transcription engine, and you get something like this:

"Yeah so I was looking at the contract and I think we need to revisit section four okay yeah I agree that clause is ambiguous from our perspective we'd want to see clearer language around the SLA terms and what happens in case of a breach right exactly and we also flagged the payment schedule as a concern..."

No speaker attribution. No conversational structure. Just a continuous stream of words. From a downstream analytics or LLM perspective, this is nearly useless. You can't perform QA on a support call if you don't know which sentences came from the agent and which from the customer. You can't build a coaching system if you can't distinguish a trainee from a supervisor. You can't summarize a podcast episode by topic if you don't know who introduced each thread.

This is where speaker diarization becomes non-negotiable. Diarization answers the fundamental question: who spoke when? It segments the audio timeline and attributes each segment to a speaker, turning that wall of text into a structured, speaker-labeled transcript.

Why mono-channel is still dominant

Before diving into how diarization works on mono audio, it's worth addressing why the industry hasn't just moved to multi-channel recording everywhere.

• Storage and infrastructure costs. Storing stereo or multi-channel audio roughly doubles (or more) the storage footprint. At the scale of a contact center processing millions of calls per month, that adds up fast. Many organizations made the trade-off years ago and aren't reversing it.

• Legacy telephony infrastructure. PSTN and many VoIP deployments mix audio server-side before it reaches any recording system. The per-speaker channels simply don't exist by the time you get the file.

• Browser and mobile capture. WebRTC-based recording tools and most mobile SDKs capture audio as a single mixed stream unless you build explicit multi-track pipelines.

• Cloud meeting platforms. Zoom, Google Meet, and Microsoft Teams all offer cloud recording, but the default output is a mixed mono or stereo file. Accessing per-participant audio requires additional configuration, elevated permissions, and often a premium tier.

The upshot is that for the foreseeable future, mono-channel audio will remain the dominant format in production Voice AI systems. Any diarization solution worth deploying needs to handle it well.

How pyannoteAI turns raw audio into speaker-attributed segments

pyannoteAI's diarization pipeline operates directly on the audio waveform. At a high level, it combines neural speaker embeddings with temporal segmentation to identify and cluster speaker-specific acoustic patterns across the recording.

The technical pipeline

The system first segments the audio into speech regions, separating speech from silence and background noise. Within those speech regions, it extracts speaker embeddings: dense vector representations that capture voice characteristics. These embeddings are then clustered to identify distinct speakers throughout the recording.

The result is a set of time-coded segments, each attributed to a speaker label. A simplified view of what comes back from the API:

{
  "diarization": [
    {
      "speaker": "SPEAKER_00",
      "start": 0.31,
      "end": 4.82
    },
    {
      "speaker": "SPEAKER_01",
      "start": 5.14,
      "end": 9.60
    },
    {
      "speaker": "SPEAKER_00",
      "start": 10.02,
      "end": 15.44
    }
  ]
}

The speaker_labels in the response are consistent identifiers across the entire recording. SPEAKER_00 in the first segment is the same voice as SPEAKER_00 in the third. This consistency is what makes downstream processing tractable.

A quick Python integration

If needed, you can test our open source SDK for the API: https://github.com/pyannote/pyannoteAI-python-sdk

Here's how to submit a mono audio file and retrieve diarization output using pyannoteAI's API:

import requests
import time

API_KEY = "YOUR_API_KEY"  # In production, use environment variables: os.getenv("PYANNOTE_API_KEY")
AUDIO_URL = "<https://files.pyannote.ai/marklex1min.wav>"

# Submit the file for diarization
response = requests.post(
    "<https://api.pyannote.ai/v1/diarize>",
    headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"},
    json={"url": AUDIO_URL}
)

if response.status_code != 200:
    print(f"Error: {response.status_code} - {response.text}")
else:
    job_id = response.json()["jobId"]

    # Poll for results
    while True:
        result = requests.get(
            f"<https://api.pyannote.ai/v1/jobs/{job_id}>",
            headers={"Authorization": f"Bearer {API_KEY}"}
        ).json()

        status = result["status"]

        if status == "succeeded":
            for segment in result["output"]["diarization"]:
                print(f"[{segment['start']:.2f}s - {segment['end']:.2f}s] {segment['speaker']}")
            break
        elif status in ["failed", "canceled"]:
            print(f"Job {status}")
            break

        print(f"Job status: {status}, waiting...")
        time.sleep(10)

Developer tip: combine diarization output with word-level timestamps from your ASR engine. Most transcription APIs return token-level timing. By aligning those timestamps with the diarization segments, you can produce fully speaker-attributed transcripts without any additional model calls. Filter by speaker == "SPEAKER_00" and you have all of that speaker's utterances in sequence, ready to feed into an LLM prompt or RAG pipeline.

Before and after: what diarization changes in practice

Customer support call

Before (raw transcript):

"Hi I'm calling about my order it hasn't arrived yet. I'm sorry to hear that can I get your order number? Sure it's 8847293. Let me pull that up it looks like there was a delay at the warehouse we can reship or refund your choice. I'd prefer a refund please. Absolutely I'll process that now."

After (diarized):

SPEAKER 0: Hi I'm calling about my order it hasn't arrived yet.

SPEAKER 1: I'm sorry to hear that, can I get your order number?

SPEAKER 0: Sure, it's 8847293.

SPEAKER 1: Let me pull that up. It looks like there was a delay at the warehouse — we can reship or refund, your choice.

SPEAKER 0: I'd prefer a refund please.

SPEAKER 1: Absolutely, I'll process that now.

With speaker attribution, QA automation becomes straightforward. You can check whether the agent followed the greeting script, measure response latency, flag calls where the customer-to-agent speaking ratio exceeds a threshold, or automatically score empathy signals in agent turns only.

Podcast episode

Before (raw transcript):

"Okay so picking up from last week I think the thing that surprised me most was actually the latency numbers yeah totally and I was going to say the same thing because when we ran our own tests we saw something completely different from what the paper reported right and that's the part I want to dig into because I think there's a methodology question here..."

After (diarized):

SPEAKER 0: Okay, so picking up from last week — I think the thing that surprised me most was actually the latency numbers.

SPEAKER 1: Yeah, totally. And I was going to say the same thing, because when we ran our own tests we saw something completely different from what the paper reported.

SPEAKER 2: Right, and that's the part I want to dig into, because I think there's a methodology question here.

Three speaker labels with consistent attribution across the episode. You can now extract each host's talking time, identify which guest statements drew the most engagement, and generate a structured summary that attributes key claims to the right speaker. Far more useful input for LLM-based summarization or content repurposing.

Handling the hard cases

Real-world mono audio is rarely clean. A few areas where production robustness matters:

• Background noise. pyannoteAI's models are trained on diverse conditions, recordings with ambient noise, and compressed codecs. The embedding extraction is designed to be noise-robust, separating vocal characteristics from environmental artifacts.

• Overlapping speech. When two speakers talk simultaneously on a mono channel, the signal is mixed and ambiguous. pyannoteAI's overlap-aware pipeline can detect and label overlapping regions, flagging them rather than silently misattributing turns. This is especially relevant for debates, panel discussions, or highly interactive conversations.

• Short turns. Back-channels, affirmations, and brief interruptions ("right," "uh-huh," "exactly") are notoriously difficult. Short segments lack sufficient audio to compute reliable embeddings. pyannoteAI's Precision-2 model handles short-turn attribution with improved accuracy, reducing the fragmentation artifacts that make short-turn-heavy conversations messy in downstream processing.

Diarization Error Rate (DER)

Handling these conditions well is only half the problem — knowing how well you're actually doing is the other half. That's where Diarization Error Rate (DER) comes in.

DER is the standard metric for evaluating diarization performance across all the hard cases described above. It measures the proportion of audio where speaker attribution is incorrect, combining three sources of error: missed speech (audio that should have been attributed but wasn't), false alarms (non-speech regions incorrectly attributed to a speaker), and speaker confusion (segments attributed to the wrong speaker).

If you're evaluating any diarization solution for production use, DER is your benchmark, but make sure you're measuring it on audio that matches your actual conditions, not on clean studio recordings. A system that scores well on a curated benchmark can degrade significantly when exposed to real telephony noise, heavy crosstalk, or short-turn-heavy conversations. For a full breakdown of how to interpret and use DER, see our guide on evaluating speaker diarization performance.

What diarization unlocks downstream

The structured, speaker-labeled output from diarization is the foundation for a wide range of Voice AI applications:

• LLM prompts and summarization: Speaker-attributed text maps cleanly to conversational context. You can instruct a model to "summarize the customer's concerns from this support call" by simply filtering the transcript to customer turns.

• RAG pipelines: Indexing speaker-attributed segments separately lets you retrieve claims by source, useful for meeting intelligence or sales call analysis.

• Agent QA and coaching: Automated scoring of agent behavior requires knowing which speech was the agent's. Without diarization, any QA automation is guessing.

• Compliance and audit trails: Financial services, healthcare, and legal sectors require attributable records of what was said by whom.

• Topic and sentiment analytics: Sentiment analysis run on mixed audio produces noisy, averaged results. Run on speaker-separated segments, it becomes meaningful.

The pattern is consistent: diarization is the transformation that converts raw audio into data that a downstream system can reason about.

Getting started

If you're building on mono-channel audio today, the practical starting point is straightforward. Submit your audio to pyannoteAI's diarization endpoint, retrieve the diarization with timestamps, and align them with your existing transcription output. From there, the speaker-attributed transcript becomes the input layer for whatever analytics, LLM, or automation layer you're building.

For foundational context on what diarization is and how it works, see What Speaker Diarization is? and How can diarization benefit your Voice AI solution?. If you're building a procurement case or benchmarking alternatives, How to evaluate speaker diarization performance covers the metrics you need.

Mono audio isn't a limitation you need to work around. With the right diarization layer, it becomes as structured and useful as any multi-channel recording.