# How To Improve Voicemail Detection Accuracy in AMD

> Stop guessing at AMD thresholds. Plura AI shows you how to build eval sets, tune settings, and measure voicemail detection accuracy in production.

**Published:** 2026-09-04 | **Updated:** 2026-10-03 | **Author:** plurasync
**URL:** https://www.plura.ai/articles/improve-voicemail-detection-accuracy
**Type:** post

**Categories:** AI Voice Agents

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## Content

*Written by: Matt Beucler, CEO, Plura AI | Last updated: September 29, 2026*

## Key Takeaways

- Improving voicemail detection accuracy starts with measuring your own false-machine, false-human, and unknown rates before changing thresholds.
- Tuning against a small set of test numbers fails because carrier, device, and language differences shift speech-and-silence patterns across calls.
- Build a labeled evaluation set of at least 200 answered calls per list type, covering mobile vs. landline, major carriers, residential vs. business, and English/Spanish to guide every tuning decision.
- Use the symptom-to-fix table to connect specific errors to changes in MachineDetectionSpeechThreshold, MachineDetectionSpeechEndThreshold, or overall detection timeout.
- Plura AI runs voicemail detection on its own FCC-licensed carrier with stateful conversation memory across voice, SMS, and RCS to support production-scale accuracy.

[See voicemail detection in action](https://www.plura.ai/plura-webchat?utm_source=ai-growth-agent&utm_term=improve-voicemail-detection-accuracy)

## Why Tuning Against a Handful of Test Numbers Fails

The most common failure mode in answering machine detection (AMD) is tuning against a small sample of test numbers. The system performs well in the lab, then breaks the moment it dials a different carrier. Twilio’s own Voice documentation acknowledges that AMD will not always return the right answer because humans and voicemail greetings follow many different patterns.4

Mobile vs. landline, carrier routing, residential vs. business lines, language, and noisy vs. clean connections all shift the speech-and-silence signature AMD reads. A small sample produces thresholds that fit those specific recordings and nothing else. Twilio ships `MachineDetectionSpeechThreshold` at a [default of 2,400 ms](https://zian.ai/answering-machine-detection-cost-ratio), and LiveKit ships `human_speech_threshold` at [2.5 seconds](https://zian.ai/answering-machine-detection-cost-ratio).4 Both values cut humans and machines with the same blade. There is no universally safe AMD setting. The only way to know which direction to tune is to measure your own traffic first.

## How To Build A Labeled Evaluation Set For Voicemail Detection Accuracy

If a small sample produces thresholds that fit nothing else, the fix is a sample large enough to represent your actual traffic. That starts with a labeled evaluation set, which forms the foundation of every tuning decision that follows.

Your eval set should cover the following dimensions so that the error rates you measure generalize to production traffic:

- Mobile vs. landline
- Major carriers
- Residential vs. business numbers
- English and Spanish
- Noisy vs. clean connections

The [recommended minimum sample size is 200 answered calls per list type, roughly three hours of listening](https://zian.ai/answering-machine-detection-cost-ratio), repeated per country and per list type because carrier behavior differs. Labeling can use human review of recordings or ASR (automatic speech recognition, the technology that converts spoken audio to text) transcript review.

Follow this procedure:

1. Pull 200 answered calls with recordings per list type.
2. Label each as human, mailbox, IVR (interactive voice response), or screening prompt.
3. Repeat per country and per list type.
4. Re-run the labeled set as a regression at every platform upgrade.

Twilio advises testing AMD with a large number of carriers, on a diverse set of devices, with varied message responses, and warns that hyper-tuning against one handset produces unexpected behavior once applications are deployed widely. The labeled eval set is what prevents that outcome.

## The Confusion Matrix For AMD: False-Machine, False-Human, And Unknown

Overall accuracy hides the errors that actually cost you money. False-machine and false-human errors carry different operational costs depending on whether the operator runs a sales dialer or a notification campaign. The table below separates the four possible AMD outcomes and shows what each one costs you in practice.

| Outcome | Definition | Operational Cost |
| --- | --- | --- |
| True-positive | Human correctly classified as human, or machine correctly classified as machine | None, the intended path |
| False-machine | A human classified as a machine; the call is dropped | A live lead is burned; the conversation and usually the contact are lost, cost equals the full value of one connected human conversation |
| False-human | A voicemail classified as a human; an agent is connected to a recording | Agent time is wasted, roughly 40 seconds of talk time per event, and no voicemail message is left |
| Unknown | Detection timed out before reaching a verdict | Depends on policy, treating uncertainty as human vs. machine produces opposite failure signatures |

Cekura recommends measuring four AMD metrics separately because they move independently.4 First, false positive rate on humans, which carries the highest compliance exposure. Second, false negative rate on machines, which represents wasted spend and is the cheaper error. Third, time to verdict at p90 and p99. Fourth, behavior on an unknown verdict. Averages hide timeouts. Track p90 and p99 latency, not means.

![Plura Conversation Intelligence dashboard displaying AI-powered call analytics, transfer tracking, and customer conversation insights.](https://cdn.aigrowthmarketer.co/1779338480670-5b2fbc1c92ba.png)**Plura Conversation Intelligence gives businesses AI-powered analytics, call transfer tracking, and customer interaction insights across every conversation.**

There is also a regulatory dimension. Under 47 CFR 64.1200(a)(7), a telemarketing call is described as “abandoned” if it is not connected to a live sales representative within two seconds of the called person’s completed greeting, and abandoning more than three percent of calls answered live by a person, measured over a 30-day period for a single calling campaign, is treated as a violation in that rule.2 Consult qualified counsel regarding how these rules apply to your specific campaigns.

![Screenshot of Plura’s fully compliant AI communications platform showing business registration and phone number provisioning workflows for AI Voice, SMS, RCS, and Webchat communication automation.](https://cdn.aigrowthmarketer.co/1779337911454-8c3a9645d906.png)**Plura’s FCC-licensed AI communications platform simplifies compliant business registration and phone number provisioning for AI Voice, SMS, RCS, and Webchat workflows.**

## How To Tune MachineDetectionSpeechThreshold And MachineDetectionSpeechEndThreshold

Twilio’s AMD defaults are `MachineDetectionSpeechThreshold` 2,400 ms, `MachineDetectionSpeechEndThreshold` 1,200 ms, `MachineDetectionSilenceTimeout` 5,000 ms, and an overall detection timeout of 30 seconds tunable between 3 and 59 seconds.

[Short voicemail greetings typically contain 1,000-2,000 ms of audio followed by 1,200-2,400 ms of silence](https://zian.ai/ai-voice-agent-voicemail-detection-failures) before additional audio plays before the beep. When `MachineDetectionSpeechEndThreshold` is set at the default 1,200 ms, that silence is treated as end-of-speech. The greeting is then classified as human. [Twilio’s own guide describes raising MachineDetectionSpeechEndThreshold to about 2,500 ms to stop short mailbox greetings reading as human](https://zian.ai/ai-voice-agent-voicemail-detection-failures), but notes that [changing it from 1,200 ms to 2,500 ms increases human detection delay by 1,300 ms](https://zian.ai/ai-voice-agent-voicemail-detection-failures), a cost paid on every human answer.

The table below maps each error you might observe in your eval set to the parameter that likely caused it and the direction to change it. Use it after you have measured your own false-machine, false-human, and unknown rates.

| Observed Symptom | Likely Cause | Parameter Change |
| --- | --- | --- |
| Live humans dropped as machines (high false-machine rate) | Speech threshold too low; short human “hello” trips a machine verdict | Increase `MachineDetectionSpeechThreshold` |
| Voicemail greetings read as humans (high false-human rate) | Speech-end threshold too low; greeting silence treated as end-of-speech | Increase `MachineDetectionSpeechEndThreshold` |
| Detection times out on long greetings (high unknown rate) | Overall detection timeout too low | Increase `MachineDetectionTimeout` |
| Human detection delayed on every answer | Speech-end threshold too high; every human pays the added delay | Decrease `MachineDetectionSpeechEndThreshold` |

The direction of every change depends on which error your labeled eval set shows. No single value works for every operation.

## Platform Settings That Shift Voicemail Detection Accuracy

Thresholds are not the only lever. Three platform settings move accuracy independently of threshold values: language matching, denoising, and the separation of two distinct objectives.

The most common conceptual error is conflating human-vs-machine classification with end-of-greeting detection. They are different problems. Twilio’s AMD documentation returns distinct labels, including `machine_end_beep`, `machine_end_silence`, `machine_end_other`, `human`, `fax`, and `unknown` in DetectMessageEnd mode. These labels allow end-of-greeting detection to be separated from the human-vs-machine classification decision. Use `machineDetection: "DetectMessageEnd"` when you need to leave a message after the beep. Use `machineDetection: "Enable"` when you only need to know whether a human or machine answered.

A baseline config block for reference:

```
machineDetection: "DetectMessageEnd" machineDetectionSpeechThreshold: 2400 machineDetectionSpeechEndThreshold: 1200 machineDetectionSilenceTimeout: 5000 machineDetectionTimeout: 30 language: "en-US" denoising: enabled
```

Language matching matters because different SIP carriers process audio differently through noise reduction, echo cancellation, and level normalization. All of these change the acoustic signal AMD analyzes. Accents, languages, and speaking patterns also vary enormously across markets. Because of that variation, set the language parameter to match the population you are dialing. If your carrier paths are noisy, enable denoising. After each change, run your labeled eval set to confirm the direction of movement before making another adjustment.

Talk to an AMD specialist

## When To Stop Tuning Thresholds And Move To An Audio Classifier

Threshold tuning has a ceiling. When your labeled eval set shows a false-machine rate above 5% after tuning, or when false-machine and false-human rates move together when you adjust thresholds, threshold tuning has stopped working. The architectural upgrade path runs in three stages.

1. **Beep-tone detection.** Identifies the post-greeting beep. Useful for message-drop timing but unreliable as a standalone classifier because hold music and carrier tones can spoof it.
2. **VAD (voice activity detection) plus temporal features.** A 2026 arXiv paper on real-time voicemail detection using temporal speech activity features ([arXiv:2604.09675](https://arxiv.org/abs/2604.09675)) reported [96.1% combined accuracy across 764 telephony recordings](https://arxiv.org/abs/2604.09675), a [0.3% false positive rate and 1.3% false negative rate validated over 77,000 production calls](https://arxiv.org/abs/2604.09675), and [46 ms end-to-end inference on a dual-core CPU](https://arxiv.org/abs/2604.09675).4 The same paper found that [adding transcription keywords or beep-based features did not improve the best real-time configuration and increased latency substantially](https://arxiv.org/abs/2604.09675).
3. **ASR transcript cues as an additional signal.** Voicemail greetings literally identify themselves with phrases like “you’ve reached,” “please leave a message,” and “after the tone.” That language signal is often more reliable than acoustic features that look similar between a long pause and a quick “hello.” Transcript-based detection lands in the 95-98% accuracy range but requires speech-to-text output to exist before a decision can be made.

**Decision rule:** If your labeled eval set shows a false-machine rate above 5% after tuning, or if your false-machine and false-human rates move together when you adjust thresholds, move to an audio classifier. Threshold tuning has stopped generalizing.

## Continuous Audit

AMD accuracy drifts as carriers change routing, as contact lists shift from landline-heavy to mobile-heavy, and as platform upgrades alter default behavior. A continuous audit process catches drift before it compounds.

Start by filtering machine-classified calls by duration. [Voicemail greetings typically wrap in 4-7 seconds](https://amdify.io/blog/how-ai-voicemail-detection-works-2026), while [calls longer than 8 seconds before the beep are often live humans](https://amdify.io/blog/how-ai-voicemail-detection-works-2026). From that filtered set, pull 200 random call recordings classified as machine and listen to each one, counting how many contain a clearly audible human voice. A rate above that threshold indicates significant revenue loss.

Re-run your labeled eval set as a regression at every platform upgrade. Track false-machine rate, false-human rate, and unknown rate as separate time-series metrics, not as a blended accuracy figure. When any of the three moves more than a point or two between audit cycles, investigate before tuning.

## Why Voicemail Detection Accuracy Matters Commercially

Voicemail detection accuracy directly controls how many real conversations your team has per hour. Every false-machine result burns a live lead. Every false-human result wastes agent time and leaves no voicemail message. [At a 20% AMD false positive rate on a 20-agent call center making 500 connected calls per hour, 100 live humans are dropped per hour](https://amdify.io/blog/how-ai-voicemail-detection-works-2026). [At 3%, 15 are dropped, recovering 85 live conversations per hour or 680 per 8-hour shift](https://amdify.io/blog/how-ai-voicemail-detection-works-2026).3 The cost per connected call is a direct function of how accurately AMD classifies the answer.

![Plura Predictive Dialer dashboard displaying AI-powered outbound call pacing, transfer analysis, and dialing performance insights.](https://cdn.aigrowthmarketer.co/1779338793506-2d33c5dff8e8.png)**Plura Predictive Dialer automates outbound calling with AI-powered pacing, transfer optimization, and real-time performance analytics.**

Plura’s [AI Predictive Dialer](https://plura.ai/ai-predictive-dialer?utm_source=ai-growth-agent&utm_term=improve-voicemail-detection-accuracy) runs voicemail detection on Plura’s own FCC-licensed carrier, which means detection decisions are made on infrastructure Plura controls rather than on a third-party CPaaS (communications platform as a service) layer. Stateful conversation memory carries context across voice, SMS, and RCS, so a contact’s prior interaction history informs every subsequent dial rather than resetting on each attempt. See how the numbers stack up for your operation with Plura’s [ROI calculator](https://plura.ai/calculator?utm_source=ai-growth-agent&utm_term=improve-voicemail-detection-accuracy).

## Frequently Asked Questions

### What Is A Good False-Machine Rate For Voicemail Detection?

A false-machine rate above 5% indicates significant revenue loss. Pull 200 random call recordings classified as machine, listen to each, and count how many contain a clearly audible human voice. If the rate exceeds that threshold, your thresholds are burning live leads. Track this metric separately from overall AMD accuracy, because a blended accuracy figure can look acceptable while the false-machine rate is high enough to materially reduce connected-call volume.

### Should I Increase Or Decrease MachineDetectionSpeechThreshold?

Increase it when live humans are being dropped as machines. A short human “hello” is tripping a machine verdict because the speech duration falls below the threshold. Decrease it when voicemail greetings are being read as humans and you need the engine to classify shorter speech segments as machine. The correct direction depends entirely on which error your labeled eval set shows. No universal best value exists, and tuning in the wrong direction makes the problem worse.

### When Should I Stop Tuning Thresholds And Move To An Audio Classifier?

Stop relying on thresholds alone when your labeled eval set shows a false-machine rate above 5% after tuning, or when false-machine and false-human rates move together as you adjust thresholds. At that point, threshold tuning has stopped generalizing. The underlying problem is that timing heuristics cannot distinguish between a short voicemail greeting and a short human greeting when both produce the same speech-and-silence signature. An audio classifier trained on temporal features, or a transcript-based classifier reading the words spoken, resolves cases that timing alone cannot.

### How Do I Build A Labeled Evaluation Set For Voicemail Detection?

The process described earlier applies. Pull 200 answered calls with recordings per list type and label each one. Repeat per country and per list type because carrier behavior and greeting conventions differ by market. Re-run the labeled set as a regression at every platform upgrade. You can use human review of recordings or ASR transcript review. The minimum sample size of 200 per list type reflects roughly three hours of listening and is the point at which the error rates you measure start to generalize to production traffic.

### How Does Plura AI Handle Voicemail Detection Differently?

Plura’s [AI Predictive Dialer](https://plura.ai/ai-predictive-dialer?utm_source=ai-growth-agent&utm_term=improve-voicemail-detection-accuracy) runs voicemail detection on Plura’s own FCC-licensed carrier rather than routing through a third-party CPaaS. That means detection decisions are made on infrastructure Plura controls, with stateful conversation memory that carries context across voice, SMS, and RCS. A contact’s prior interaction history, qualification status, and prior offers are available at the moment of detection rather than resetting on every dial. Plura supports TCPA compliance and DNC compliance as first-class platform layers, with real-time DNC scrubbing applied before each outbound contact.1 Customers remain responsible for their own compliance obligations and regulatory posture.

## Conclusion: Measure First, Then Tune

Improving voicemail detection accuracy starts with quantifying your own false-machine, false-human, and unknown rates against a labeled evaluation set that reflects your actual carrier mix, list type, and geography. Tuning against a handful of test numbers produces thresholds that do not generalize. The symptom-to-fix table in this guide maps each observed error to a specific parameter change. When threshold tuning stops moving the false-machine rate below that threshold, the architectural upgrade to an audio classifier is the next step.

Plura’s [AI Predictive Dialer](https://plura.ai/ai-predictive-dialer?utm_source=ai-growth-agent&utm_term=improve-voicemail-detection-accuracy) is built for operators who need voicemail detection to work at production scale. Detection decisions carry context instead of resetting on every dial. For operators running high-volume outbound campaigns, that difference compounds across every shift.

[Start your accuracy audit with Plura](https://www.plura.ai/plura-webchat?utm_source=ai-growth-agent&utm_term=improve-voicemail-detection-accuracy)

[Check your ROI in real time](https://plura.ai/calculator?utm_source=ai-growth-agent&utm_term=improve-voicemail-detection-accuracy)

1 Plura AI maintains SOC 2, HIPAA, ISO, and GDPR posture as part of its platform infrastructure. References to compliance frameworks in this article describe Plura’s platform capabilities and do not constitute a guarantee that any customer using Plura will themselves be compliant with applicable laws or standards. Customers remain solely responsible for their own regulatory obligations, certifications, consent management, recordkeeping, and the claims they make to their own end users. Consult qualified legal counsel for guidance specific to your use case.

2 This article describes regulatory frameworks at a general level and does not constitute legal advice. Laws and regulations vary by jurisdiction, change over time, and apply differently depending on facts and circumstances. Readers should consult qualified legal counsel before making compliance decisions.

3 Performance figures, customer outcomes, and industry statistics referenced in this article are drawn from cited third-party sources or Plura customer case studies. Individual results vary based on implementation, use case, industry, audience, and execution. Past or aggregate performance is not a guarantee of future results.

4 References to third-party products, services, companies, or research are made for informational and comparative purposes only. Plura AI is not affiliated with, endorsed by, or sponsored by any third party named in this article unless explicitly stated. Trademarks and product names referenced remain the property of their respective owners.

This article is provided for informational purposes only and reflects Plura AI’s understanding at the time of publication. Product capabilities, integrations, and specifications are subject to change. For the most current information, visit plura.ai.

This article was produced with the assistance of AI tools and reviewed by Plura AI prior to publication.

## Read Next

- [Voicemail Detection Best Practices: 2026 Tuning Guide](https://www.plura.ai/articles/best-voicemail-detection-practices/?utm_source=ai-growth-agent&utm_term=improve-voicemail-detection-accuracy)
- [Voicemail Detection Best Practices for AI Predictive Dialers](https://www.plura.ai/articles/voicemail-detection-best-practices/?utm_source=ai-growth-agent&utm_term=improve-voicemail-detection-accuracy)
- [Voicemail Detection Accuracy: 2026 Benchmarks and Tuning](https://www.plura.ai/articles/voicemail-detection-accuracy-guide/?utm_source=ai-growth-agent&utm_term=improve-voicemail-detection-accuracy)
- [Predictive Dialer Voicemail Detection: How AMD Works](https://www.plura.ai/articles/predictive-dialer-voicemail-detection/?utm_source=ai-growth-agent&utm_term=improve-voicemail-detection-accuracy)
- [How Voicemail Detection Works: Signals, AI, and AMD](https://www.plura.ai/articles/how-voicemail-detection-works/?utm_source=ai-growth-agent&utm_term=improve-voicemail-detection-accuracy)

---

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## Citations

- [How AI Voice Appointment Scheduling Works End to End](https://www.plura.ai/articles/ai-voice-appointment-scheduling-2026)
- [Missed Call Booking System: Turn Lost Calls Into Revenue](https://www.plura.ai/articles/missed-call-booking-system)
- [How To Enable Call Screening On iPhone And Fix Issues](https://www.plura.ai/articles/enable-call-screening-iphone)
- [IVR Automation for Call Centers: Costs and Options in 2026](https://www.plura.ai/articles/ivr-automation-call-centers)
- [After-Hours Virtual Receptionist: AI vs. Live vs. Hybrid](https://www.plura.ai/articles/after-hours-call-answering-receptionist)

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