{"id":3438,"date":"2026-09-10T05:07:58","date_gmt":"2026-09-10T05:07:58","guid":{"rendered":"https:\/\/www.plura.ai\/articles\/voicemail-detection-parameters"},"modified":"2026-09-10T05:07:58","modified_gmt":"2026-09-10T05:07:58","slug":"voicemail-detection-parameters","status":"publish","type":"post","link":"https:\/\/www.plura.ai\/articles\/voicemail-detection-parameters","title":{"rendered":"Voicemail Detection Parameters: Cross-Platform Guide"},"content":{"rendered":"<p><em>Written by: Matt Beucler, CEO, Plura AI<\/em><\/p>\n<p><em>Updated 2026-09-09<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>Voicemail detection parameters (AMD) control how outbound systems separate live humans from answering machines using four thresholds: timeout, speech, silence, and beep detection.<\/li>\n<li>Misconfigured parameters create wasted calls on voicemail and premature hang-ups on live prospects across every major platform.<\/li>\n<li>Twilio, Asterisk, Plivo, and Vonage expose different parameter names and defaults, so each platform needs its own tuning strategy.<sup data-disclaimer-id=\"25\" data-disclaimer-index=\"2\">2<\/sup><\/li>\n<li>Carrier-grade detection removes manual AMD tuning by analyzing audio patterns automatically instead of exposing raw thresholds.<\/li>\n<li>Plura AI\u2019s <a href=\"https:\/\/www.plura.ai\/plura-webchat\" target=\"_blank\">AI Predictive Dialer<\/a> delivers carrier-grade voicemail detection that removes the tuning burden so teams can focus on live conversations.<\/li>\n<\/ul>\n<h2>The Four Core Voicemail Detection Parameters Used Across Platforms<\/h2>\n<p>Every AMD implementation relies on the same four parameters, even though vendors label them differently. These shared concepts make it easier to translate settings from one platform to another.<\/p>\n<h3>Voicemail Detection Timeout<\/h3>\n<p>The timeout parameter, also called detection timeout or total analysis time, caps how long the system listens before returning a default result. If no decision is reached within this window, the call is classified as \u201cunknown\u201d or \u201cnot sure.\u201d On some cloud APIs, voicemail detection timeouts can be configured up to 60 seconds (for example, Twilio caps machineDetectionTimeout at 60 s), while on-premises systems like Asterisk default to 5,000 ms (5 seconds) for total analysis time. A timeout that is too low increases unknown results. One that is too high adds dead air, which can cause live humans to hang up before an agent connects.<\/p>\n<h3>Voicemail Detection Speech Threshold Settings<\/h3>\n<p>The speech threshold defines the minimum duration of speech activity required to classify audio as a human greeting versus a machine greeting. Cloud platforms like Twilio express this in milliseconds, with machineDetectionSpeechEndThreshold defaulting to 1,200 ms, while Asterisk uses a combination of minimum word length (default 100 ms) and maximum word count (default 3 per the tuning guide, though the official amd.conf reference lists 2). A speech threshold set too low increases false machine detections on short human responses like \u201cHello?\u201d A threshold set too high can miss brief voicemail greetings entirely.<\/p>\n<h3>Voicemail Detection Silence Threshold<\/h3>\n<p>The silence threshold governs how much silence triggers a decision. Two distinct silence parameters exist: initial silence (how long the system waits for audio before the greeting begins) and after-greeting silence (how much quiet after speech indicates the greeting has ended). <a href=\"https:\/\/asterisk-doxygen.osso.pub\/master\/api\/d1\/de4\/amd_8conf.html\" target=\"_blank\" rel=\"noindex nofollow\">Asterisk defaults to 2,500 ms of initial silence and 800 ms of after-greeting silence<\/a>. <a href=\"https:\/\/docs.plivo.com\/docs\/voice\/api\/calls\" target=\"_blank\" rel=\"noindex nofollow\">Plivo exposes <code>machine_detection_initial_silence<\/code> (range 2,000-10,000 ms) and <code>machine_detection_silence<\/code> (range 500-5,000 ms)<\/a>. Incorrect silence values often drive false \u201chuman\u201d detections on short voicemail greetings.<\/p>\n<h3>Voicemail Beep Detection<\/h3>\n<p>Beep detection determines whether the system listens for the distinct voicemail beep tone that follows a recorded greeting. When enabled, the system waits for the beep before triggering a message-drop action. Some platforms, like Twilio with <code>machineDetection=DetectMessageEnd<\/code>, listen for the end of the greeting message, while others listen specifically for the beep tone. Beep detection is critical for campaigns that drop pre-recorded messages, but it adds analysis time that can cause live humans to hang up if misconfigured.<\/p>\n<p><a href=\"https:\/\/plura.ai\/ai-predictive-dialer\" target=\"_blank\" rel=\"noindex nofollow\">See carrier-grade AMD in action<\/a> on your own outbound campaigns.<\/p>\n<figure style=\"text-align: center\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1779338793506-2d33c5dff8e8.png\" alt=\"Plura Predictive Dialer dashboard displaying AI-powered outbound call pacing, transfer analysis, and dialing performance insights.\" style=\"max-height: 500px\" loading=\"lazy\"><figcaption><em>Plura Predictive Dialer automates outbound calling with AI-powered pacing, transfer optimization, and real-time performance analytics.<\/em><\/figcaption><\/figure>\n<h2>Twilio Voicemail Detection Parameters<\/h2>\n<p>Twilio\u2019s Voice API exposes AMD through the <code>&lt;Dial&gt;<\/code> verb\u2019s machine detection attributes. Twilio\u2019s official documentation defines the following key parameters:<\/p>\n<table>\n<thead>\n<tr>\n<th>Parameter<\/th>\n<th>Default<\/th>\n<th>Description<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><code>machineDetection<\/code><\/td>\n<td>Not set<\/td>\n<td>Set to <code>Enable<\/code> for detection only, or <code>DetectMessageEnd<\/code> to detect the end of the greeting message<\/td>\n<\/tr>\n<tr>\n<td><code>machineDetectionTimeout<\/code><\/td>\n<td>30 s (range 3\u201359 s)<\/td>\n<td>Maximum time to wait for a machine detection result; default is 30 seconds with an allowed range of 3 to 59 seconds<\/td>\n<\/tr>\n<tr>\n<td><code>machineDetectionSpeechThreshold<\/code><\/td>\n<td>2,400 ms<\/td>\n<td>Minimum speech duration to classify audio as a machine greeting<\/td>\n<\/tr>\n<tr>\n<td><code>machineDetectionSpeechEndThreshold<\/code><\/td>\n<td>1,200 ms<\/td>\n<td>Silence required after speech to determine the greeting has ended<\/td>\n<\/tr>\n<tr>\n<td><code>machineDetectionSilenceTimeout<\/code><\/td>\n<td>5,000 ms<\/td>\n<td>Initial silence window before declaring no-speech<\/td>\n<\/tr>\n<tr>\n<td><code>asyncAmd<\/code><\/td>\n<td>false<\/td>\n<td>Enables asynchronous AMD with status callbacks<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Twilio\u2019s limitations documentation notes that AMD does not always return the correct answer and that tuning involves tradeoffs. A low <code>machineDetectionTimeout<\/code> increases unknown results. A low <code>machineDetectionSpeechThreshold<\/code> increases false machine detections on short human responses. A high <code>machineDetectionSpeechEndThreshold<\/code> delays human detection and can misinterpret human silences as machines.<\/p>\n<h2>Asterisk AMD Parameters<\/h2>\n<p>Moving from a cloud API to an on-premises PBX, Asterisk\u2019s AMD module exposes a broader set of controls that require similar attention. Asterisk\u2019s built-in AMD module is configured in <code>amd.conf<\/code>. <a href=\"https:\/\/asterisk-doxygen.osso.pub\/master\/api\/d1\/de4\/amd_8conf.html\" target=\"_blank\" rel=\"noindex nofollow\">The official Asterisk configuration reference<\/a> documents the following parameters and defaults:<\/p>\n<table>\n<thead>\n<tr>\n<th>Parameter<\/th>\n<th>Default<\/th>\n<th>Description<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><code>initial_silence<\/code><\/td>\n<td>2,500 ms<\/td>\n<td>Maximum silence before greeting; if exceeded, call is classified MACHINE<\/td>\n<\/tr>\n<tr>\n<td><code>greeting<\/code><\/td>\n<td>1,500 ms<\/td>\n<td>Maximum greeting length; if exceeded, call is classified MACHINE<\/td>\n<\/tr>\n<tr>\n<td><code>after_greeting_silence<\/code><\/td>\n<td>800 ms<\/td>\n<td>Silence after greeting that triggers HUMAN classification<\/td>\n<\/tr>\n<tr>\n<td><code>total_analysis_time<\/code><\/td>\n<td>5,000 ms<\/td>\n<td>Maximum time for AMD to reach a decision<\/td>\n<\/tr>\n<tr>\n<td><code>silence_threshold<\/code><\/td>\n<td>256 (scale 0\u201332767)<\/td>\n<td>Audio energy level below which signal is treated as silence<\/td>\n<\/tr>\n<tr>\n<td><code>min_word_length<\/code><\/td>\n<td>100 ms<\/td>\n<td>Minimum voice duration to count as a word<\/td>\n<\/tr>\n<tr>\n<td><code>maximum_word_length<\/code><\/td>\n<td>5,000 ms<\/td>\n<td>Maximum duration of a single utterance<\/td>\n<\/tr>\n<tr>\n<td><code>between_words_silence<\/code><\/td>\n<td>50 ms<\/td>\n<td>Silence required between detected words<\/td>\n<\/tr>\n<tr>\n<td><code>maximum_number_of_words<\/code><\/td>\n<td>3 (per tuning guide; official reference lists 2)<\/td>\n<td>Maximum words in greeting before MACHINE classification<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Asterisk\u2019s AMD() application sets two variables after analysis: <code>AMDSTATUS<\/code> (HUMAN, MACHINE, or NOTSURE) and <code>AMDCAUSE<\/code>, which records the specific threshold that produced the decision, such as INITIALSILENCE, LONGGREETING, MAXWORDS, or TOOLONG.<\/p>\n<h2>Plivo Voicemail Detection Settings<\/h2>\n<p>Plivo\u2019s Voice API exposes machine detection through the <code>machine_detection<\/code> parameter, which accepts <code>true<\/code> (detect and continue) or <code>hangup<\/code> (detect and disconnect). <a href=\"https:\/\/docs.plivo.com\/docs\/voice\/api\/calls\" target=\"_blank\" rel=\"noindex nofollow\">Plivo\u2019s official API documentation<\/a> defines the following tuning parameters:<\/p>\n<table>\n<thead>\n<tr>\n<th>Parameter<\/th>\n<th>Default \/ Range<\/th>\n<th>Description<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><code>machine_detection<\/code><\/td>\n<td><code>true<\/code> or <code>hangup<\/code><\/td>\n<td>Enables AMD or hangs up when a machine is detected<\/td>\n<\/tr>\n<tr>\n<td><code>machine_detection_time<\/code><\/td>\n<td>5,000 ms (range 2,000\u201310,000 ms)<\/td>\n<td>Time allotted for machine detection<\/td>\n<\/tr>\n<tr>\n<td><code>machine_detection_initial_silence<\/code><\/td>\n<td>Range 2,000\u201310,000 ms<\/td>\n<td>Initial silence before classification<\/td>\n<\/tr>\n<tr>\n<td><code>machine_detection_initial_greeting<\/code><\/td>\n<td>Range 1,000\u20135,000 ms<\/td>\n<td>Maximum greeting duration to analyze<\/td>\n<\/tr>\n<tr>\n<td><code>machine_detection_maximum_speech_length<\/code><\/td>\n<td>Range 1,000\u20136,000 ms<\/td>\n<td>Maximum speech duration before determination<\/td>\n<\/tr>\n<tr>\n<td><code>machine_detection_maximum_words<\/code><\/td>\n<td>Range 2\u201310 words<\/td>\n<td>Maximum words before determination<\/td>\n<\/tr>\n<tr>\n<td><code>machine_detection_silence<\/code><\/td>\n<td>Range 500\u20135,000 ms<\/td>\n<td>Silence after speech indicating end of greeting<\/td>\n<\/tr>\n<tr>\n<td><code>machine_detection_answer_time<\/code><\/td>\n<td>Range 100\u201345,000 ms<\/td>\n<td>Total time for AMD analysis<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><a href=\"https:\/\/plivo.com\/docs\/voice-agents\/audio-streaming\/deploy\/us-call-quality-and-cps\" target=\"_blank\" rel=\"noindex nofollow\">Plivo\u2019s US call quality documentation<\/a> notes that hanging up on every voicemail generates short-duration calls that can trigger carrier spam flags and per-call surcharges. Their guidance states: \u201cDo not hang up on every voicemail. Leave a brief message or schedule a retry instead.\u201d<\/p>\n<h2>Vonage AMD Parameters<\/h2>\n<p>Vonage\u2019s Voice API does not expose configurable voicemail detection parameters comparable to dedicated AMD products. A review of Vonage\u2019s official developer documentation shows that Vonage\u2019s Voice API focuses on call control and PSTN branded calling rather than machine detection. Teams using Vonage for outbound campaigns typically implement AMD at the orchestration layer or use a third-party detection service. Vonage\u2019s branded calling feature, built with First Orion\u2019s INFORM platform, improves answer rates by attaching verified identity information to outbound calls, which reduces the likelihood of calls being screened to voicemail.<sup data-disclaimer-id=\"25\" data-disclaimer-index=\"2\">2<\/sup><\/p>\n<h2>Plura AI Voicemail Detection<\/h2>\n<p>Plura AI\u2019s <a href=\"https:\/\/plura.ai\/ai-predictive-dialer\" target=\"_blank\" rel=\"noindex nofollow\">AI Predictive Dialer<\/a> handles <a href=\"https:\/\/plura.ai\/ai-predictive-dialer\" target=\"_blank\" rel=\"noindex nofollow\">voicemail detection<\/a> at the carrier level as part of its FCC-licensed infrastructure.<sup data-disclaimer-id=\"23\" data-disclaimer-index=\"1\">1<\/sup> Unlike the platforms above, which require manual AMD parameter tuning, Plura uses carrier-grade analysis to distinguish live humans from voicemail systems automatically. Operations teams skip the tuning cycle entirely: they no longer calibrate speech thresholds, experiment with silence timeouts, or troubleshoot false positives. The <a href=\"https:\/\/plura.ai\/ai-predictive-dialer\" target=\"_blank\" rel=\"noindex nofollow\">AI Predictive Dialer<\/a> connects agents only to live humans, which reduces wasted calls and dead air from manually configured AMD systems.<\/p>\n<figure style=\"text-align: center\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1779339309900-eefcb08741d1.png\" alt=\"Plura Predictive Dialer dashboard showing AI-powered outbound dialing, intelligent call routing, and performance analytics.\" style=\"max-height: 500px\" loading=\"lazy\"><figcaption><em>Plura Predictive Dialer uses AI-powered outbound dialing, intelligent routing, and real-time analytics to maximize call performance.<\/em><\/figcaption><\/figure>\n<p><a href=\"https:\/\/plura.ai\/ai-predictive-dialer\" target=\"_blank\" rel=\"noindex nofollow\">Schedule a predictive dialer walkthrough<\/a> tailored to your outbound use cases.<\/p>\n<h2>Cross-Platform Voicemail Detection Parameter Comparison<\/h2>\n<p>The table below summarizes default timeout, speech, and silence values for each platform so you can see how much manual tuning each stack expects from your team.<\/p>\n<table>\n<thead>\n<tr>\n<th>Platform<\/th>\n<th>Default Timeout<\/th>\n<th>Default Speech Threshold<\/th>\n<th>Default Silence Timeout<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Twilio<\/td>\n<td>30 s (range 3\u201359 s)<\/td>\n<td>2,400 ms (<code>machineDetectionSpeechThreshold<\/code>)<\/td>\n<td>5,000 ms (<code>machineDetectionSilenceTimeout<\/code>)<\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/asterisk-doxygen.osso.pub\/master\/api\/d1\/de4\/amd_8conf.html\" target=\"_blank\" rel=\"noindex nofollow\">Asterisk<\/a><\/td>\n<td>5,000 ms (<code>total_analysis_time<\/code>)<\/td>\n<td>100 ms min word length (<code>min_word_length<\/code>)<\/td>\n<td>2,500 ms initial \/ 800 ms after greeting<\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/docs.plivo.com\/docs\/voice\/api\/calls\" target=\"_blank\" rel=\"noindex nofollow\">Plivo<\/a><\/td>\n<td>5,000 ms (<code>machine_detection_time<\/code>)<\/td>\n<td>1,000\u20136,000 ms configurable (<code>machine_detection_maximum_speech_length<\/code>)<\/td>\n<td>2,000\u201310,000 ms configurable (<code>machine_detection_initial_silence<\/code>)<\/td>\n<\/tr>\n<tr>\n<td>Vonage<\/td>\n<td>Not exposed<\/td>\n<td>Not exposed<\/td>\n<td>Not exposed<\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/plura.ai\/ai-predictive-dialer\" target=\"_blank\" rel=\"noindex nofollow\">Plura AI<\/a><\/td>\n<td>Automatic (carrier-grade)<\/td>\n<td>Automatic (carrier-grade)<\/td>\n<td>Automatic (carrier-grade)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>How to Reduce False Positives in Voicemail Detection<\/h2>\n<p>False \u201chuman\u201d detections, where the system misclassifies a voicemail greeting as a live person, create the most pain in AMD configuration. Short greetings like \u201cLeave a message\u201d are the most common trigger. The playbook below outlines platform-specific adjustments.<\/p>\n<h3>Twilio False-Positive Fix<\/h3>\n<p>Twilio\u2019s limitations documentation identifies the root cause. A low <code>machineDetectionSpeechThreshold<\/code> increases false machine detections on short human responses, while a high <code>machineDetectionSpeechEndThreshold<\/code> delays human detection. For short voicemail greetings, increase <code>machineDetectionSpeechThreshold<\/code> to 3,000\u20134,000 ms and reduce <code>machineDetectionSpeechEndThreshold<\/code> to 1,500\u20132,000 ms. Also verify webhook response time, and host media in the region closest to your Twilio region.<\/p>\n<h3>Asterisk False-Positive Fix<\/h3>\n<p><a href=\"https:\/\/asterisk-doxygen.osso.pub\/master\/api\/d1\/de4\/amd_8conf.html\" target=\"_blank\" rel=\"noindex nofollow\">Asterisk\u2019s <code>after_greeting_silence<\/code> parameter<\/a> (default 800 ms) often drives misclassification. When a voicemail speaker pauses between sentences, the silence can exceed 800 ms and Asterisk classifies the call as HUMAN. Increase <code>after_greeting_silence<\/code> to 1,200\u20131,500 ms to tolerate pauses in recorded greetings. Also consider raising <code>maximum_number_of_words<\/code> from the default of 3 to 5\u20136, since short greetings like \u201cHello, this is Jordan speaking\u201d can exceed the default word count and trigger a false MACHINE classification.<\/p>\n<h3>Plivo False-Positive Fix<\/h3>\n<p>Plivo\u2019s <a href=\"https:\/\/docs.plivo.com\/docs\/voice\/api\/calls\" target=\"_blank\" rel=\"noindex nofollow\"><code>machine_detection_silence<\/code> parameter<\/a> (range 500\u20135,000 ms) controls the silence after speech that indicates the end of a greeting. For short voicemail greetings, increase this value to 2,000\u20133,000 ms so that brief pauses within the greeting are not interpreted as the end of the message. Also adjust <code>machine_detection_maximum_speech_length<\/code>. If this value is set too low, the system may classify a short greeting as a human response before the full message plays.<\/p>\n<h3>General Tuning Principles<\/h3>\n<p>Change one parameter at a time and validate against labeled recordings from your actual campaign. Background noise, short greetings, and campaign-specific speech patterns vary by environment. Track false-machine and false-human rates separately. A false machine loses a live contact, while a false human consumes agent time and creates dead air.<\/p>\n<h2>Best Practices for Testing Voicemail Detection Across Carriers<\/h2>\n<p>Carrier behavior varies significantly, and AMD settings that work on one network may fail on another. Use this testing workflow to keep results consistent:<\/p>\n<ol>\n<li>Export real recordings from the last 90 days of the same campaign, trunk mix, destination region, and calling hours.<\/li>\n<li>Label each recording according to what a reviewer hears, such as human greeting, traditional voicemail greeting, call-screening prompt, silence or failed media, or ambiguous audio, rather than the existing dialer disposition.<\/li>\n<li>Test with complete audio including carrier delay. Measuring only the greeting from an edited recording removes the silence behavior being evaluated.<\/li>\n<li>Measure false-machine and false-human rates separately, along with NOTSURE rate and decision time.<\/li>\n<li>Watch for False Answer Supervision (FAS). When a carrier returns an answer signal before a human picks up, AMD analysis starts on hold music, ringback bleed, or silence, which corrupts the analysis window.<\/li>\n<\/ol>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What Is the Best Voicemail Detection Timeout?<\/h3>\n<p>The optimal timeout depends on your platform and campaign. Twilio caps <code>machineDetectionTimeout<\/code> at 60 seconds with a default of 30 seconds. Asterisk defaults <code>total_analysis_time<\/code> to 5,000 ms (5 seconds). Plivo\u2019s <code>machine_detection_time<\/code> defaults to 5,000 ms with a range of 2,000\u201310,000 ms. A shorter timeout reduces dead air but increases \u201cunknown\u201d results. A longer timeout catches more voicemail greetings but increases the risk of live humans hanging up during analysis. The right value depends on greeting patterns for your destination numbers and carrier mix. Start at the platform default, measure your NOTSURE rate, and adjust in 500 ms increments while tracking both false-machine and false-human outcomes separately.<\/p>\n<h3>How Do I Reduce False Positives in Voicemail Detection?<\/h3>\n<p>False positives, or misclassifying voicemail as human, are most often caused by short greetings with internal pauses. The primary fix is to increase the after-greeting silence threshold on your platform: <code>after_greeting_silence<\/code> on Asterisk, <code>machineDetectionSpeechEndThreshold<\/code> on Twilio, and <code>machine_detection_silence<\/code> on Plivo. This change gives the system more time to confirm that the greeting has ended before returning a HUMAN result. Also verify that your speech threshold is not set too low, which can classify brief human responses as machines. Always change one parameter at a time and validate against a labeled recording set from your actual campaign before deploying to production.<\/p>\n<h3>How Do Voicemail Detection Systems Tell a Human from a Machine?<\/h3>\n<p>AMD systems analyze audio patterns such as speech duration, silence between phrases, number of distinct words, and the presence of a beep tone. A typical voicemail greeting runs about 10\u201330 seconds, with most professional guidance recommending 15\u201325 seconds, and it ends with a beep. A live human typically answers with a short \u201cHello?\u201d followed by silence while waiting for the caller to speak. AMD parameters encode these distinctions as speech thresholds, silence timeouts, and word counts. When a greeting\u2019s duration, word count, or silence pattern exceeds the configured thresholds, the system classifies the call as a machine. When the audio is short and followed by silence, the system classifies it as human. Short voicemail greetings and brief human responses can produce nearly identical audio signatures, so tuning becomes an ongoing process rather than a one-time configuration.<\/p>\n<h3>What Does a NOTSURE Result Mean in Asterisk AMD?<\/h3>\n<p>A NOTSURE result means Asterisk\u2019s AMD module reached the <code>total_analysis_time<\/code> limit without enough evidence to classify the call as either HUMAN or MACHINE. The <code>AMDCAUSE<\/code> variable typically reports TOOLONG in this case. NOTSURE represents a legitimate operational outcome rather than a technical error. Routing every NOTSURE result to MACHINE hides uncertainty and can discard live contacts. Many teams route NOTSURE calls to a dedicated queue, such as a short-duration agent review or a retry with a longer <code>total_analysis_time<\/code>, and track the NOTSURE rate separately. A rising NOTSURE rate often signals that <code>total_analysis_time<\/code> is too short for greeting patterns on the current carrier mix.<\/p>\n<h3>Does Plura AI Offer Voicemail Detection?<\/h3>\n<p>Yes. Plura AI\u2019s <a href=\"https:\/\/plura.ai\/ai-predictive-dialer\" target=\"_blank\" rel=\"noindex nofollow\">AI Predictive Dialer<\/a> includes carrier-grade voicemail detection as a built-in feature. Because Plura operates its own FCC-licensed carrier infrastructure, voicemail detection runs automatically without manual parameter tuning.<sup data-disclaimer-id=\"23\" data-disclaimer-index=\"1\">1<\/sup> The AI Predictive Dialer connects agents only to live humans, which reduces wasted calls on answering machines. Teams that have spent engineering cycles tuning Twilio, Asterisk, or Plivo AMD parameters often shift that effort to higher-value work once Plura handles detection at the carrier level.<\/p>\n<h2>Stop Tuning and Focus on Live Conversations<\/h2>\n<p>Voicemail detection parameters often determine whether a dialer wastes thousands of calls on answering machines or consistently connects agents to live humans. This guide has outlined the exact parameter names, defaults, and tuning values for Twilio, Asterisk, Plivo, and Vonage, along with a troubleshooting playbook for common failure modes. Manual AMD tuning remains a constant operational burden as carrier behavior changes, greeting patterns shift, and new campaigns introduce fresh edge cases. Plura\u2019s <a href=\"https:\/\/plura.ai\/ai-predictive-dialer\" target=\"_blank\" rel=\"noindex nofollow\">AI Predictive Dialer<\/a> uses carrier-grade voicemail detection that works out of the box so leaders can focus on staffing, conversion, and revenue. <a href=\"https:\/\/plura.ai\/ai-predictive-dialer\" target=\"_blank\" rel=\"noindex nofollow\">Request a live Plura demo<\/a> to review your current AMD performance and explore a carrier-level approach.<\/p>\n<hr data-disclaimer-divider=\"true\">\n<div data-disclaimer-footer=\"true\">\n<p data-disclaimer-id=\"23\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"1\">1<\/sup> 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.<\/p>\n<p data-disclaimer-id=\"25\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"2\">2<\/sup> 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.<\/p>\n<p data-disclaimer-id=\"21\" data-disclaimer-type=\"fixed\">This article is provided for informational purposes only and reflects Plura AI\u2019s understanding at the time of publication. Product capabilities, integrations, and specifications are subject to change. For the most current information, visit plura.ai.<\/p>\n<p data-disclaimer-id=\"27\" data-disclaimer-type=\"fixed\">This article was produced with the assistance of AI tools and reviewed by Plura AI prior to publication.<\/p>\n<\/div>\n<section data-read-next=\"true\">\n<h2>Read Next<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/improve-voicemail-detection-accuracy\" target=\"_blank\">How to Improve Voicemail Detection Accuracy: 2026 Guide<\/a><\/li>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/voicemail-detection-integration\" target=\"_blank\">Voicemail Detection Integration for Contact Centers<\/a><\/li>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/voicemail-beep-detection\" target=\"_blank\">Voicemail Beep Detection: How It Works and Why It Fails<\/a><\/li>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/voicemail-detection-voice-ai\" target=\"_blank\">What Is Voicemail Detection in Voice AI?<\/a><\/li>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/best-voicemail-detection-practices\" target=\"_blank\">Voicemail Detection Best Practices: 2026 Tuning Guide<\/a><\/li>\n<\/ul>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Compare voicemail detection parameters across Twilio, Asterisk, Plivo, and Vonage. Plura AI handles AMD so your team focuses on live calls.<\/p>\n","protected":false},"author":106,"featured_media":3437,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-3438","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-sales-automation"],"_links":{"self":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts\/3438","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/comments?post=3438"}],"version-history":[{"count":0,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts\/3438\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/media\/3437"}],"wp:attachment":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/media?parent=3438"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/categories?post=3438"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/tags?post=3438"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}