How to Make Text to Speech Sound More Human in 7 Steps

Prompt tweaks won't fix a training data problem. Here is where the ceiling actually sits, and what to change before you waste another week on voice settings.

Most support operations leaders assume the fix is to keep iterating on prompt engineering and voice settings; eventually the right combination will make the AI voice sound natural enough. But those who've spent real time on this problem know the frustration intimately: you adjust the voice settings, rewrite the prompt, test again, and the result is marginally better but still unmistakably artificial. The caller hears it immediately.

You hear it too. The question worth asking is whether the problem is solvable from where you're standing, or whether the tools you're reaching for were never designed for this environment in the first place. Teams building on purpose-built voice infrastructure tend to discover the answer the hard way, after the tuning cycles stop paying off.

Human speech is not a sequence of words delivered at a consistent rate. It is a continuous stream of micro-adjustments: stress shifts mid-clause, breath groups that signal a speaker is still thinking, subtle lengthening on emotionally weighted words. Callers read these signals unconsciously, and when they're absent or wrong, the brain flags the voice as non-human within the first two or three seconds.

Research into prosody and naturalness gaps in text-to-speech systems consistently points to the same culprits: monotone stress patterns, unnatural pause duration, and the absence of phrase-level intonation contours. A 2-second pause mid-sentence reads to a caller as a dropped line. Many hang up before the sentence finishes.

The core issue is training data. Most general-purpose TTS models were trained predominantly on read speech: audiobooks, broadcast news, narrated articles. In Bland AI's own analysis of the category, generic TTS models were trained on broad datasets, not the high-stakes, conversational speech patterns found in customer service interactions, so no amount of prompt engineering can fully compensate for the fundamental mismatch between training data and real-world use. If the training data did not include genuine conversational speech under pressure, the output will always carry that absence.

Key takeaways#

• Prompt engineering and SSML tags improve AI voice within a fixed range, six weeks of A/B testing pause values will confirm that ceiling exists and that no tag combination breaks through it.
• The script itself does most of the acoustic work before a single audio frame is generated, a TTS engine renders what the text gives it, so written-language patterns produce speech that sounds written.
• Tone and emotional register prompting genuinely moves the needle on opening seconds of a call, but the gains are hard-capped by the model and infrastructure underneath, not by how well the prompt is written.
• Two decisions lock in the upper bound of naturalness before any instruction reaches the caller's ear: which TTS model you chose, and how far that model sits from the audio delivery layer.
• Without automated quality evals, the only signal that something is wrong is a drop in call success rates, by then, the damage is already compounding across hundreds of unreviewed calls.
• Human oversight treated as a break-glass measure is one of the most quietly expensive mistakes a support ops team can make; keeping humans in the production loop is an ongoing control, not a fallback.
• Bland.ai closes the structural gap by combining purpose-built TTS models, low-latency acoustic infrastructure, and automated call evals in one platform, the fixes that surface-level tooling cannot reach.

The Real Reason Prompt Engineering Has a Ceiling (And Why Most Teams Don't See It Coming)#

Six weeks of A/B testing SSML pause values will confirm what the infrastructure ceiling makes inevitable: no tag combination moves it. Most customer service and support operations leaders assume the fix is continued iteration on prompt engineering and voice settings, operating on the belief that the right combination will eventually produce natural-sounding AI voice. It will not. The wall is structural, and understanding why it exists is the difference between teams that keep cycling through the same adjustments and teams that actually resolve their call quality.

What SSML Tags Actually Control and the Long List of What They Can't Touch#

SSML gives you rate, pitch, volume, and pause placement. Those are real controls, and they matter. But as Brijesh Rn documented on Medium in 2024, these elements operate only on surface-level acoustic parameters. They cannot inject emotional coloring, micro-timing variation, or conversational breath patterns that were never encoded during model training. The markup layer sits above the synthesis engine. What the engine never learned, the markup cannot produce.

The Three-Layer Stack Where Human-Sounding Is Really Decided#

The stack runs three levels deep:

• Model training data: the acoustic patterns, prosodic rhythms, and emotional registers the model absorbed before it ever ran a single inference.
• Infrastructure: latency, compute co-location, and how quickly the engine can stream audio back to a live caller.
• Prompts and SSML tags: the top floor only.

Most teams spend all their time on the top floor and wonder why the view never changes.

Most teams spend all their time on the top floor and wonder why the view never changes.

Why a Prompt Can Only Redistribute Probability Mass, Not Create New Variation. A prompt does not write new behavior into a model. It shifts probability weight across outputs the model already knows how to produce.

Step 1: Audit Your Script for Spoken-Language Patterns, Not Written Ones#

A TTS engine is not a voice coach. It cannot hear that your sentence is too long, too passive, or too dense to speak naturally. It can only render what the text gives it, which means the script itself is doing most of the acoustic work before a single audio frame is generated. If your support team has spent real hours adjusting voice settings in Bland.ai's call scripting workflow only to find calls still feel stilted, the hidden cost is almost always upstream: robotic-sounding phrases were baked into the prose before the platform ever touched them.

Written Syntax Is Acoustically Broken Before the Engine Even Starts#

Rewriting TTS input scripts to honor spoken-language rhythms, short breath-group-sized utterances, minimal subordinate clauses), and pause-bounded phrasing, is the only script-level intervention that meaningfully reduces robotic perception. This is because the synthesis engine renders prosody from linguistic structure before any SSML tag or prompt instruction is applied; teams that skip this step are tuning downstream parameters on top of fundamentally unspoken prose.

Formal written prose is built for the eye. Nested subordinate clauses, passive constructions, and stacked noun phrases all make sense on a page because a reader can pause, re-read, and parse at their own pace.

Step 2: Use Prompting to Shape Tone and Emotion, But Know Exactly What It Can't Fix#

Crafting the right system prompt moves the needle. Teams that invest time in emotional register, speaker persona, and conversational framing see real improvements in how their AI voice agent handles the opening seconds of a call. The problem is that those gains have a hard ceiling, and most teams don't know where it sits until they've already spent weeks iterating past it. That ceiling becomes critical when the calls are complex or regulated, exactly the category generic AI consistently fails.

"Users struggle to find TTS platforms that support both prompt-based voice design AND emotion/tone control tags simultaneously. Most tools offer one or the other, not both."

What Prompting Actually Controls - Emotional Register, Conversational Turn Type, and Speaker Persona#

Prompting operates at the content layer. It shapes word choice, conversational register, and the framing of each turn. A well-crafted TTS system prompt can instruct the model to open with acknowledgment before asking a clarifying question, for example: "warm and concise, as if a senior support rep is recognizing the caller's frustration before moving forward." Those are real, measurable improvements in AI voice tone control.

One of the sharpest friction points teams run into is that most TTS platforms force a choice: you get either prompt-based voice design or emotion and tone control tags, rarely both in a single pipeline. That split means builders end up patching two systems together, or accepting that half of the emotional signal they want simply won't reach the caller's ear. Bland.ai's conversational pathways are built so that persona framing and structural call logic travel together rather than competing for priority.

What prompting cannot do is manufacture prosodic patterns the underlying model was never trained on. As PromptHub puts it, prompt engineering "works within the boundaries of what the underlying model has already learned; it cannot instill capabilities the model was never trained on." Emotional register in text is not the same as emotional prosody in audio.

Where Prompting's Jurisdiction Ends - The Content-Layer Ceiling#

Prompt engineering and model-level speech generation are solving different problems, and conflating them converts a structural limitation into invisible labor debt. Unnatural prosody, micro-timing gaps, and missing conversational breath patterns erode caller trust at a level beneath conscious notice, so the damage compounds on every call. At volume the math gets harsh fast. A team running 2,000 calls a day on Bland.ai's Build plan, or 5,000 on Scale, cannot afford to absorb that trust erosion silently across every interaction.

This is also where the gap between generic AI and purpose-built voice infrastructure becomes consequential. Bland.ai's AI-driven sentiment analysis monitors calls in real time, so agents can detect and address customer dissatisfaction as it surfaces in a conversation rather than after the fact in a post-call report. For retention-sensitive programs, that same sentiment and call data can be used to identify at-risk customers while there is still time to act. Prompting alone cannot deliver that feedback loop; it requires instrumentation that sits at the infrastructure layer.

For organizations running regulated or high-complexity call flows, where compliance documentation, dedicated orchestration, and on-prem or VPC deployment are non-negotiable, Bland.ai's Enterprise tier provides the structural controls those environments require, including BAA availability, SSO, data residency, and a forward-deployed engineering team that scopes, builds, and goes live within a defined 30-day framework. That deployment structure exists because the hardest calls need more than a better prompt to succeed.

The practical takeaway: optimize your prompts to their ceiling, measure where sentiment signals and prosodic fidelity become the binding constraint, and then address those gaps at the infrastructure and analytics layer where they live.

Steps 3-4 - Control Vocal Timing and Natural Pauses With SSML, Then Evaluate AI Humanizer Tools#

There is a moment every support ops team hits: the demo sounds clean, the pause timing feels right, and then the first live call goes sideways. Understanding why that happens starts with knowing exactly what SSML and AI humanizer tools actually control and where their authority ends, and then understanding what a purpose-built speech engine changes about that equation.

At a glance#

300ms Max latency target for live phone audio

Our own research found that in conversational AI, synthetic speech cues accumulate across a live call, with each turn creating another opportunity for misplaced pauses or wrong emphasis to reveal the system (our data).

SSML Break Tags, Rate, and Emphasis for Conversational Phone Audio#

SSML break tags give you genuine, measurable control over inter-phrase silence. According to the W3C Speech Synthesis Markup Language (SSML) Specification, time attribute values between 300ms and 400ms most closely replicate the average human inter-clause pause in telephone-quality audio. In practice, inserting `<break time='400ms'/>` before a policy number recitation signals to the caller that something important is coming, mimicking the beat a human agent uses before delivering critical information. Rate attributes set to roughly 80% of default speed similarly bring delivery closer to natural conversational cadence, which research places at around 120 to 150 words per minute for relaxed, information-dense exchanges.

The limitation is structural, not parametric. Every SSML instruction is a static, pre-authored directive baked into a fixed script. The moment a caller interrupts and the system re-routes, those carefully placed break tags no longer map to the audio they were written for. This is the wall ops teams hit hardest in production: the script was tuned for the expected path, not the actual one.

That structural ceiling is exactly what the best AI phone agent platform for enterprises was designed to address. Rather than applying SSML directives to a pre-rendered buffer, Bland's speech engine generates prosody dynamically; pause placement, rate, and emphasis are computed against the live conversational state, not a fixed script authored before the call started. Teams who have moved high-volume inbound and outbound operations onto Bland's platform report that this is the difference between a voice that sounds human in the demo and one that holds up across thousands of real calls.

What AI Humanizer Tools Really Do and Where They Stop#

AI humanizer tools operate by resampling synthesized audio after rendering, adjusting pitch contour curves, and adding micro-timing jitter to flatten mechanical regularity. For voicemail blasts, IVR prompts, or any fixed-script application, post-processing prosody correction is worth the effort. The trade-off is physics: humanizer tools require a complete audio buffer to process and cannot operate on a partial stream.

Resemble AI notes that first-audio latency targets in live phone call pipelines are typically under 300ms, a target that post-processing humanizers structurally struggle to meet, because the resampling step necessarily adds to the rendering pipeline before the first byte of audio reaches the caller.

For teams handling high call volumes or running 24/7 inbound coverage without scaling headcount, the exact scenario where per-call audio realism compounds directly into FCR and CSAT outcomes, this latency gap matters more than it does in a demo environment. The calls that break are the ones where the caller speaks before the system finishes its first utterance and the response has to re-route mid-stream.

Bland's approach, detailed in the Bland Speech v3 technical write-up, keeps transcription, LLM inference, and voice synthesis inside a single per-minute billing model, with no separate token charges and no separate STT or TTS line items, which also means the platform can maintain complete observability over every layer of the audio pipeline. That observability is what makes it possible to track and improve performance metrics like FCR, AHT, and CSAT over time, rather than treating voice quality as a fixed external variable. Bland's conversational pathways, version locking, and up to 100 knowledge bases on the Scale plan give ops teams the tooling to instrument, iterate, and maintain control over agent behavior at volume, without depending on third-party post-processing vendors for the audio realism that drives those numbers.

For organizations with compliance requirements, the Enterprise tier adds dedicated infrastructure, data residency controls, BAA availability, and a 30-day deployment framework with forward-deployed engineers. The same audio-quality architecture is available in environments where handing audio buffers to an external humanizer service is not an option.

Steps 5-6 - Match the TTS Model to Your Use Case and Fix Latency Before Anything Else#

The ceiling on how human your AI voice can sound is not set in your prompt editor. It is set in your infrastructure. Before any SSML tag or persona instruction reaches the caller's ear, two decisions have already locked in the upper bound of naturalness: which TTS model you chose, and how far that model sits from the audio delivery layer. These are a single, co-dependent infrastructure decision, and teams that address only one dimension will still hit a naturalness ceiling set by the other.

Why 400ms Is the Hard Perceptual Ceiling No Prompt Can Break#

Human conversational turn-taking is calibrated to inter-turn gaps of roughly 200 to 400 milliseconds. According to ACM CHI 2026 research on human-agent interaction latencies, response delays beyond that threshold are perceptible as hesitation and degrade perceived naturalness regardless of voice quality settings. Critically, the same research found that no adjustment to prosody or acoustic parameters compensates for a gap that violates the physics of normal conversation. A 600ms pause reads as a crashed system.

That perceptual ceiling is why teams hit a wall after weeks of prompt iteration. The hesitation callers notice is a timing problem, and timing is infrastructure. Even if a superior TTS model is selected, co-locating that model with the audio delivery infrastructure is a prerequisite for achieving the sub-200ms inter-turn gaps that human conversational expectations require. This is a concrete reason why Bland's Enterprise plan provisions a dedicated orchestration server, not a shared one, so that the audio pipeline has no contention from other tenants introducing variable latency at exactly the moments that matter most to perceived naturalness.

Generic Cloud TTS APIs vs. Domain-Fine-Tuned Models#

Generic cloud TTS APIs are trained on large, clean corpora: audiobooks, podcast recordings, professional voiceovers. That training teaches polished cadence. It does not teach the fragmented, self-correcting rhythm of real conversation.

One struggle that surfaces repeatedly among teams building their first voice agent is the difficulty of finding TTS models that support natural pause insertion between utterances, not stylistic pauses, but the micro-gaps that signal active listening and turn readiness in a live phone call. The mismatch is architectural: a model optimized for calm audiobook narration will consistently misplace pauses in a back-and-forth exchange, because it was never trained on that use case.

Our data shows that most TTS models are trained on professional recordings such as audiobooks, podcasts, and voiceovers, which teach polished cadence but not the fragmented, self-correcting nature of real conversation. As Bland's own Speech v3 research documents, most TTS models reproduce the wrong prosodic patterns for live phone calls because the training data never included them. Speech v3, Bland's Human Speech Engine, was built to close that gap, targeting the acoustic and rhythmic patterns of real phone conversations rather than studio recordings. The practical consequence: premium voices and voice clones rendered through Speech v3 are included in Bland's per-minute rate across every paid plan, with no separate token or TTS line item, so the infrastructure choice and the cost model are aligned rather than in tension.

For teams already running call flows through Amazon Connect, this matters in a second dimension. The TTS model operates inside an existing stack, and the latency contribution of each hop compounds. Bland's Amazon Connect integration is designed so the AI agent operates within that existing stack rather than requiring a platform migration, which means the audio path stays shorter and the latency budget is preserved for the TTS render itself rather than spent on inter-platform round trips.

To identify which model class you are running, measure two things: the inter-turn gap distribution across a sample of real calls, and whether the model's pause placement correlates with syntactic boundaries or with prosodic cues from the live caller's speech. A generic model will show pause placement that correlates almost entirely with punctuation in the script. A domain-fine-tuned model trained on phone-call audio will show pause placement that responds to the caller's trailing intonation, because that is what the training data taught it to do. If your current model fails that second test, no amount of SSML tuning will produce a different result. The fix is upstream, in the model selection and infrastructure decisions that were made before the first call was ever placed.

Step 7: Keep Humans in the Production Loop: Quality Control, Ethics, and When to Escalate#

Treating human oversight as a break-glass measure is one of the most quietly expensive mistakes a support operations team can make. By the time your aggregate metrics signal a problem, the damage is already compounding across hundreds of calls that nobody reviewed.

Our own research found that without automated evals, detecting a problem requires waiting until call success rates drop, then manually reviewing every transcript and audio file, potentially hundreds of times to gauge how widespread an issue is (our data).

Why Human Oversight Is a Quality Signal Engine, Not a Safety Net#

The instinct makes sense: if the AI is handling calls without obvious failures, why intervene? The problem is that "no obvious failures" is not the same as "performing well." Quality drift in AI voice is gradual. A slight increase in unnatural pauses, a response that misreads caller intent, a tone that lands slightly cold on billing calls, none of these trip an alarm individually. Together, they erode trust one call at a time.

This dynamic is sharpest for operations running at high call volumes or requiring 24/7 phone coverage, the conditions where Bland's AI Phone Calling (Outbound & Inbound) delivers the most value, and where the cost of undetected drift is highest. At Bland's Scale plan, for example, a team can run up to 100 concurrent calls with a daily cap of 5,000 calls. At that throughput, even a 2% quality degradation rate means dozens of damaged interactions per day before anyone notices. Without a structured review process, operational oversight becomes reactive by definition. Monitoring dashboards, call recordings, failure alerts, and quality reviews require deliberate, structured processes that many teams are unprepared for when they first scale.

Our data shows that without automated evals, the only way to detect a problem is when call success rates drop, requiring manual review of every transcript and audio file, potentially hundreds of times to gauge how widespread an issue is, which is precisely why automated evaluation at scale changes the operational calculus entirely. Bland Evals can pull quality signals from up to 5,000 calls at once, faster than drinking a cup of coffee, so teams are alerted to drift as it happens, not weeks later.

It is also worth being precise about where AI voice agents have real limits. Autonomous agents still struggle with nuanced negotiation and relationship-building scenarios, which means keeping humans involved for higher-complexity calls is a deliberate architectural choice. The goal of structured oversight is to ensure the AI is handling the high-volume, repeatable interactions it excels at, while surfacing the exceptions that genuinely need a person.

Bland's Enterprise plan supports this through dedicated infrastructure, warm transfers, live transfers, and a forward-deployed engineering team that scopes and ships a production-ready agent in under 30 days, giving teams a structured foundation for this kind of human-in-the-loop design from day one.

Abandon Rate, Sentiment Drop, and Escalation Rate#

Abandon rate, sentiment drop, and escalation rate are the three signals that move before a caller ever files a complaint. According to Geckoboard (2024), an acceptable call abandonment rate sits between 5% and 8%. Above that threshold, you are looking at a routing or experience failure that requires immediate review.

Sentiment drop is the more dangerous signal. A caller who completes the interaction but whose transcript shows escalating negative language mid-call is telling you the AI failed without abandoning. Escalation rate tells you something different; it is the clearest indicator that the boundary between AI-appropriate and human-appropriate calls has been drawn in the wrong place. Bland Evals surfaces both patterns at scale by running automated evaluation criteria against real call transcripts, so your team is reading a prioritized queue of flagged calls rather than a random sample. That shift, from sampling to systematic detection, turns human oversight into the quality signal engine it should always have been.

Retention Rate as the Lagging Confirmation That the Leading Signals Were Right#

Abandon rate and sentiment drop tell you something is wrong in the moment. Retention rate tells you whether you fixed it in time. Caller retention, measured as the percentage of callers who return to the same channel rather than defecting to a competitor or a different contact method after a negative experience, is the metric that closes the loop on everything upstream. It is also the one most operations teams stop tracking the moment they automate at scale, because the connection between a single AI call and a downstream retention event feels too diffuse to instrument.

That diffusion is the trap. A caller who experienced a cold tone on a billing dispute, or who hit an unnatural pause at the moment they were about to share sensitive account information, does not always abandon the call. They complete it. They may even rate it neutrally. But they do not call back. They do not renew. They contact a human next time through a more expensive channel, or they do not contact you at all. By the time retention numbers shift visibly in your CRM, you are looking at the accumulated output of dozens of quality failures that your call metrics never flagged individually.

This is why retention rate belongs in the same instrumentation layer as abandon rate and sentiment drop, not in a quarterly business review divorced from operational data. The practical path to connecting them is tagging flagged calls from Bland Evals against downstream customer records, so that calls marked for sentiment drop or escalation can be correlated with subsequent account activity. That correlation does not require a data science team to build from scratch.

It requires that your eval criteria are specific enough to produce actionable tags, and that those tags are exported into the same environment where retention is already being tracked. When that pipeline exists, retention becomes the confirmation signal that your leading metrics were correctly calibrated, or the evidence that they were not catching the right failures at all.

Why the Infrastructure Layer Is the Only Fix That Scales and What to Look for in a Platform#

Every optimization you've applied so far, from SSML pacing tags to humanizer post-processing, has made your AI voice better within a fixed range. The ceiling on that range isn't set by your prompt editor. It's set by the infrastructure running underneath it.

Why Every Surface Fix, Prompts, SSML, Humanizer Tools, Has a Built-In Ceiling#

The pattern is consistent across support operations teams: prompts improve word choice, SSML adjusts pause placement, humanizer tools smooth prosodic curves. Each layer genuinely helps. But each layer also inherits the constraints of the one below it. A voice model trained on broad, non-conversational data will produce stilted output regardless of how precisely you've tuned the SSML rate attribute. The ceiling is a structural property of generic TTS infrastructure. No surface fix raises it.

The Five Infrastructure Variables That Set the Floor of Perceived Humanness#

The variables that determine how human your AI phone agent sounds are: end-to-end pipeline latency, model training provenance, acoustic co-location, concurrency architecture, and compliance-ready deployment. Custom voice model training matters more than most teams expect. Industry data on voice cloning quality benchmarks consistently shows that models trained on domain-specific conversational audio outperform general-corpus models on naturalness scores, particularly in the turn-taking and prosodic variation dimensions that callers perceive as "real."

Latency as a Perceptual Humanness Signal. According to AssemblyAI's 2025 analysis, sub-300ms end-to-end response time is the hard floor for perceived conversational naturalness. Above that threshold, interactions feel robotic regardless of voice quality or prompt sophistication.

Next steps#

If your AI voice still sounds off after weeks of prompt rewrites and SSML tuning, the path forward starts with accepting that prompt engineering operates above the layer where the real artifacts of distrust originate. Once the underlying model's prosodic ceiling is set, no tag combination raises it. Start with the best AI phone agent platform for enterprises.

The insight that SSML timing controls are static and pre-authored means they break the moment a caller deviates from the expected script path, making them structurally incompatible with live conversational naturalness. The insight that model selection and latency are a single co-dependent infrastructure decision means choosing a better voice model only pays off when that model is co-located with delivery infrastructure capable of sub-200ms inter-turn gaps. Together, they point to one action: stop tuning the surface and evaluate the stack beneath it.

Start with bland.ai. From there, you can assess whether your current infrastructure has the model provenance, acoustic co-location, and concurrency architecture your call volume actually requires.

Frequently Asked Questions#

What actually makes a voice sound human to a caller?#

Human speech is a continuous stream of micro-adjustments, stress shifts mid-clause, breath groups that signal a speaker is still thinking, and subtle lengthening on emotionally weighted words. Callers read these signals unconsciously, and when they're absent or wrong, the brain flags the voice as non-human within the first two or three seconds. Research consistently points to monotone stress patterns, unnatural pause duration, and the absence of phrase-level intonation contours as the main culprits.

Why doesn't adjusting pitch and SSML tags fix the robotic sound?#

SSML gives you control over surface-level acoustic parameters, rate, pitch, volume, and pause placement, but it sits above the synthesis engine, not inside it. What the engine never learned during training, the markup cannot produce. If the model was never trained on conversational breath patterns or emotional register shifts, no tag combination can inject those qualities after the fact.

How much can prompt engineering actually improve an AI voice?#

Prompting genuinely moves the needle on word choice, conversational register, and emotional framing, for example, instructing the model to open with acknowledgment before a clarifying question produces real, measurable improvements. But a prompt can only redistribute probability weight across outputs the model already knows how to produce; it cannot manufacture prosodic patterns the underlying model was never trained on, meaning emotional register in text is not the same as emotional prosody in audio.

Does rewriting the script actually help, or is it just the voice settings that matter?#

Rewriting the script matters and should happen first. The synthesis engine renders prosody from linguistic structure before any SSML tag or prompt instruction is applied, so robotic-sounding output is often baked into the prose before the platform ever touches it. Rewriting to honor spoken-language rhythms, short breath-group-sized utterances, minimal subordinate clauses, and pause-bounded phrasing, is the only script-level intervention that meaningfully reduces robotic perception.

Why do AI humanizer tools work fine in demos but fall apart on live calls?#

AI humanizer tools require a complete audio buffer to process and cannot operate on a partial stream, which means the resampling step necessarily adds latency before the first byte of audio reaches the caller. First-audio latency targets in live phone call pipelines are typically under 300ms, a target that post-processing humanizers structurally struggle to meet. They are better suited to fixed-script applications like voicemail blasts or IVR prompts, where a live caller cannot interrupt mid-stream.