Voice Changer and Noise Suppression: Pair Them, But Get the Order Right
Voice changer noise suppression is one of those topics where the answer seems obvious — use both — until you actually do it and your audio comes out sounding underwater. The real question is not whether to combine them; it is how, specifically in what order, and whether you need two separate tools or can handle both in one app. This guide answers all three and explains the CPU math behind it so you can make an informed call for your own setup.
TL;DR
- Noise suppression and voice changer work great together — but noise suppression must run first, then voice changer.
- Running them backwards introduces artifacts the suppressor cannot fix.
- VoxBooster’s built-in NS enforces correct ordering automatically.
- Separate tools (Krisp, RTX Voice) work too — just route audio so NS comes before the voice changer.
- CPU cost for both together is manageable on any mid-range machine built in the last four years.
- If you only have CPU for one: prioritize noise suppression for calls and meetings; prioritize the voice changer for entertainment and streaming personas.
Why the Order Is Non-Negotiable
Start here because it is the single most important thing in this entire article.
When you speak into a microphone, your signal contains two things: your voice and everything else — fan hum, keyboard clicks, air conditioning, room echo. A noise suppressor’s job is to remove that second category before anything downstream has to deal with it. A voice changer’s job is to transform your voice into something else.
If noise suppression runs first: The voice changer receives a clean signal. It can model your voice accurately, apply the transformation, and output clean modified audio. Every algorithm benefits from clean input — this is true for pitch shifting, formant manipulation, and AI-based voice conversion alike.
If the voice changer runs first: The voice changer processes your voice along with all the background noise baked into it. It transforms both. The resulting output contains distorted noise artifacts — keyboard clicks shifted in pitch, fan hum stretched across new harmonics, room echo modulated by the transformation. Now you send that mess to the noise suppressor, which was trained to recognize voice and remove noise. The problem: the “voice” it receives now has harmonic artifacts that overlap with what it learned to classify as noise. It starts attenuating the wrong things. The result is the classic “watery” or “robotic but not in a cool way” quality that fills Reddit threads with complaints.
The chain is: Microphone → Noise Suppression → Voice Changer → Virtual Mic / App output.
This ordering principle also appears in post-production workflows. See the Audacity voice changer tutorial for a deeper look at effect ordering in offline editing, and our noise suppression software guide for a complete breakdown of how suppression algorithms work.
What Noise Suppression Actually Removes (and What It Cannot)
Before comparing tools, worth being precise about what noise suppression software targets.
What it handles well:
- Steady-state noise: fans, AC units, white noise, laptop coolers
- Moderate keyboard and mouse click noise (especially with AI/ML models)
- Mic hiss and preamp self-noise
- Distant TV or music in the same room
What it struggles with:
- Your own voice overlapping with a nearby talker (two voices in the same frequency range)
- Very loud intermittent impacts close to the mic
- Room reverb / echo (suppression ≠ de-reverb; these are different signal processing problems)
- Noise below the mic capsule’s noise floor — software cannot recover what was never captured
Knowing these limits matters when you layer noise suppression with a voice changer, because if your room has significant reverb, neither tool fully solves it. The voice changer will transform the reverb tail along with your voice, and the suppressor will leave most of it intact. Room treatment — even just recording in a closet with hanging clothes — eliminates problems that no software chain can fix.
The Three Main Noise Suppression Options and How They Pair with a Voice Changer
Option 1: VoxBooster Built-In Noise Suppression
VoxBooster includes noise suppression as part of its processing pipeline, and critically, it enforces the correct ordering internally. You do not have to think about routing. Enable NS in VoxBooster’s settings and it runs before your voice effect or AI voice conversion, every time, automatically.
This is the simplest path. One app, correct chain order, no configuration of virtual audio cables or Voicemeeter routing tables. The built-in suppressor uses an RNNoise-derived model that handles steady-state noise and moderate keyboard noise without perceptible latency.
The tradeoff: VoxBooster’s built-in NS is solid but not class-leading for very difficult acoustic environments. If you record in a room with a loud gaming PC two feet from the mic and a mechanical keyboard hammering at 150 WPM, you may want a heavier AI/ML suppressor upstream.
Option 2: NVIDIA RTX Voice (Free, GPU-Offloaded)
NVIDIA RTX Voice is embedded in the NVIDIA Broadcast application and available at no cost to RTX GPU owners. It uses a deep learning model running on the RTX tensor cores, meaning it adds essentially zero CPU load. The quality is excellent — it handles keyboard noise, fan noise, background speech, and dynamic environments better than RNNoise.
To pair it with a voice changer: set RTX Voice as your microphone source in NVIDIA Broadcast, then set that Broadcast virtual mic as the input in VoxBooster. Audio flows: physical mic → RTX Voice NS → VoxBooster voice changer → output. The order is correct.
The constraint: RTX GPU required. If you have one, this is the best-quality free suppression option available. If you have a GTX card or AMD GPU, you need a different approach.
Option 3: Krisp (CPU-Based, Cross-Platform)
Krisp is a subscription noise suppressor (~$14/month, free tier available) that runs its own deep learning model entirely on CPU, no GPU required. The quality is comparable to RTX Voice for most environments. Krisp presents a virtual microphone that apps can select; that virtual mic outputs the cleaned signal.
To pair with a voice changer: set Krisp’s virtual mic as the input source in VoxBooster. Physical mic → Krisp (NS) → VoxBooster (voice changer) → output. Order is correct.
The constraints: CPU load is real — Krisp typically adds 5–10% CPU usage on a modern core. It also requires an internet connection for the first-run model download, though processing is local after that. The subscription cost is an ongoing expense that stacks on top of your voice changer.
Comparison Table
| Tool | Cost | CPU Impact | GPU Required | Quality | Integration |
|---|---|---|---|---|---|
| VoxBooster Built-in NS | Included | Low (~1–3%) | No | Good | Automatic correct order |
| NVIDIA RTX Voice | Free | Near-zero | RTX GPU | Excellent | Manual routing needed |
| Krisp | ~$14/mo (free tier) | Moderate (5–10%) | No | Excellent | Manual routing needed |
| OBS RNNoise Filter | Free | Very low (<1%) | No | Good for steady noise | OBS only, not system-wide |
| NVIDIA Broadcast (full) | Free | Near-zero | RTX GPU | Excellent | Separate virtual mic |
For OBS-only workflows where you broadcast but do not need noise suppression in Discord or calls, the built-in OBS RNNoise filter is a legitimate free option. It does not help with system-wide audio (calls, games), but it is excellent for stream output.
CPU Cost: Do Both Together Without Throttling Your Game
The practical concern for gamers and streamers: does running noise suppression alongside a voice changer tank frame rates or cause audio dropouts?
Here is the realistic math for a mid-range system (Intel Core i5-12400 / Ryzen 5 5600 class):
| Component | Approximate CPU Load |
|---|---|
| RNNoise suppression | <1% |
| Krisp deep learning NS | 5–10% |
| RTX Voice (GPU offloaded) | <1% CPU |
| Voice effect (pitch shift / EQ type) | 3–8% |
| AI voice conversion | 10–25% |
| OBS encoding (x264 medium) | 15–35% |
| Modern game | 40–70% |
The takeaway: noise suppression + a pitch-shift or effect-based voice changer together add roughly 5–15% CPU overhead. For AI voice conversion the number is higher — 15–35% combined with a heavy NS — but still workable on a modern CPU when the game itself is not pegged at 100%.
Where you get into trouble: running AI voice conversion + Krisp + OBS x264 encoding + a CPU-heavy game at the same time on an older quad-core CPU. The solution is usually either switching to GPU-based NS (RTX Voice) to reclaim CPU headroom, or switching to a lighter voice effect type rather than full AI conversion during gaming sessions.
For a detailed breakdown of voice changer latency and performance optimization, see our voice changer latency tuning guide.
When to Pick Just One: Noise Suppression vs Voice Changer
If you genuinely cannot run both (old hardware, high CPU game, streaming at high bitrate), which one should you keep?
Choose noise suppression when:
- The use case is work calls, team meetings, or customer-facing communication
- You care more about being understood clearly than sounding like a character
- Your room is genuinely noisy (loud PC, shared space, street noise)
- Other participants on the call are reporting audio quality complaints
Choose the voice changer when:
- You are streaming or gaming with an entertainment persona
- Privacy or anonymity is the primary goal
- The background noise in your room is already low (treated space, good mic, quiet environment)
- The transformation itself is the point of the session (content creation, VTubing, roleplay)
The honest answer for most people doing Discord gaming with a decent mic in a reasonably quiet room: you can get away with just the voice changer. The honest answer for anyone doing content creation professionally, on remote calls, or in a noisy environment: noise suppression is the higher-priority tool, with the voice changer layered on top.
Also consider microphone selection — a better mic reduces the noise suppression workload. Our guide on choosing a budget microphone for voice changer use covers which mic features matter most when you plan to process the signal.
How VoxBooster Handles the Chain Internally
VoxBooster’s audio processing pipeline handles the ordering problem so you do not have to architect it manually:
- Input capture — grabs raw audio from your physical microphone via WASAPI
- Noise suppression — applies the built-in NS model to the raw signal
- Voice processing — applies your selected effect or AI voice conversion to the clean signal
- Output — presents the result on a virtual microphone that Windows sees as a standard audio input device
Because everything runs inside one app, there is no virtual cable routing, no Voicemeeter mixer session to maintain, and no risk of accidentally loading the apps in the wrong order. The chain is enforced at the code level.
The virtual microphone VoxBooster presents uses WASAPI and does not require a kernel-mode driver. This matters for anti-cheat compatibility — games running Easy Anti-Cheat, BattlEye, or Vanguard can see and use the virtual mic without triggering driver-level violations that kernel drivers would cause.
For setups where you want noise suppression professional-quality beyond what the built-in NS provides, the Krisp or RTX Voice routing described above is fully compatible with VoxBooster as the downstream voice changer. The apps stack cleanly.
Practical Setup Walkthrough: Voice Changer + External NS
If you decide to use Krisp or RTX Voice upstream from VoxBooster, here is the exact routing:
With Krisp:
- Install Krisp and open its settings. In the Microphone section, select your physical microphone as Krisp’s input source.
- Krisp will create a virtual microphone called “Krisp Microphone.”
- In VoxBooster, go to Settings → Audio Input and select “Krisp Microphone” as your input device.
- Enable your voice effect or AI voice conversion in VoxBooster as normal.
- In Discord, OBS, or your game, select VoxBooster’s virtual microphone as the input.
Chain: Physical Mic → Krisp (NS) → VoxBooster (voice changer) → Application.
With NVIDIA Broadcast / RTX Voice:
- Open NVIDIA Broadcast. In the Microphone section, select your physical microphone and enable noise removal.
- NVIDIA Broadcast creates a virtual microphone called “NVIDIA RTX Microphone.”
- In VoxBooster, set Audio Input to “NVIDIA RTX Microphone.”
- Enable voice effects in VoxBooster.
- In applications, select VoxBooster’s output.
Chain: Physical Mic → RTX Voice (NS) → VoxBooster (voice changer) → Application.
Both setups are stable in Windows 10 and 11. The only occasional issue: NVIDIA Broadcast sometimes resets its source selection after a driver update — worth checking if audio quality suddenly degrades after an NVIDIA update.
Does Noise Suppression + Voice Changer Actually Sound Better Together?
Yes — measurably and audibly, when set up correctly. Here is why:
Voice changer algorithms, particularly AI-based ones, model your vocal characteristics from the input signal. If the input contains broadband noise, that noise is modeled alongside your voice. On a clean input, the algorithm spends all its capacity on your actual voice. The output formant accuracy, the naturalness of the transformation, and the absence of background artifacts all improve.
Think of it like photography: a noise suppressor is equivalent to a clean lens. Even if your camera has a great sensor (voice changer), shooting through a dirty lens (noisy mic signal) produces worse results than a mediocre sensor behind a clean lens. Clean signal in, clean transformed signal out.
The subjective difference is most noticeable with AI voice conversion — the style of voice changing that produces the most naturalistic results. With a dirty input, AI conversion tends to produce metallic or “fizzy” artifacts in consonant-heavy speech. With noise-suppressed input, those artifacts largely disappear.
For a broader look at how audio processing choices affect streaming quality and professional call presence, see our guide on sounding professional on calls.
EQ as a Third Layer: Where It Fits
Some setups add an EQ stage to the chain as well. Where does it belong?
The conventional answer for voice work: EQ after the voice changer, as a final tonal shaping step. Noise suppression first removes the noise floor; the voice changer transforms the voice character; EQ fine-tunes the spectral output of the transformed voice to taste — boosting presence frequencies, cutting low-end mud, or rolling off harshness introduced by the transformation.
Running EQ before the voice changer is uncommon and usually counterproductive — you would be shaping the input voice to the transformation algorithm, which generally prefers a flat, clean input rather than a pre-shaped one.
For a detailed comparison of when EQ is the right tool versus a voice changer (and when to use neither), see voice changer vs EQ: when to use each.
Frequently Asked Questions
Can I use a voice changer and noise suppression at the same time?
Yes — and most experienced streamers do. The critical rule is ordering: noise suppression must run first to clean the mic signal, then the voice changer transforms that clean audio. Reverse the order and the voice changer creates new harmonic artifacts that the suppressor then fights, producing a watery, degraded result.
Does noise suppression affect voice changer quality?
Run before the voice changer, noise suppression improves its quality significantly. A clean input signal means the voice transformation algorithm only has to deal with your voice — not keyboard clicks, fan hum, or room echo baked into every harmonic. Dirty input produces dirty output, no matter how good the voice changer is.
What is the best noise suppression for use with a voice changer?
For an all-in-one setup, VoxBooster handles the ordering internally so you do not have to manage a separate tool. If you prefer separate apps, NVIDIA RTX Voice and Krisp both run well upstream. RTX Voice requires an RTX GPU but is essentially free; Krisp costs around $14/month and works on any CPU.
Does running noise suppression and a voice changer together use a lot of CPU?
It depends on the implementations. RNNoise-based suppression uses under 1% of a modern CPU core. AI/ML suppressors like Krisp or RTX Voice add 5–15% CPU or offload to GPU. A real-time voice changer adds another 5–20% depending on the effect type. Total load on a mid-range CPU is manageable but worth monitoring.
Should I use Krisp, RTX Voice, or built-in noise suppression with my voice changer?
RTX Voice is the best option if you have an RTX GPU — GPU-offloaded, low CPU overhead, free. Krisp is the best cross-hardware option if you want no GPU dependency. VoxBooster’s built-in suppressor is the easiest path if you already use VoxBooster — correct ordering is guaranteed and you skip the two-app management overhead entirely.
Why does my voice sound watery or robotic when using noise suppression with a voice changer?
This almost always means the suppressor is running after the voice changer instead of before it. The voice changer adds complex harmonics; the suppressor then classifies some of those harmonics as noise and attenuates them. Fix the chain order — NS before voice changer — and the watery quality disappears.
Does noise suppression work in real time for live streaming?
Yes. Modern noise suppression tools like RNNoise (built into OBS), Krisp, NVIDIA RTX Voice, and VoxBooster’s built-in NS all operate in real time with latency between 10–30 ms. That is imperceptible in live conversation. Post-production denoisers designed for studio work can add 100 ms or more and are not suitable for live use.
Conclusion
Voice changer noise suppression is not an either/or choice for most setups — you combine them, with noise suppression running first to deliver a clean signal to the voice changer. That order is the single rule that separates great-sounding live audio from the watery, artifact-riddled mess that most noise-in-voice-changer setups produce before someone figures it out.
The tool choice is secondary: VoxBooster’s built-in NS is the simplest path because the ordering is enforced automatically. RTX Voice upstream is the best quality option if you have the GPU. Krisp upstream is the best option if you do not. All three can be used correctly with a voice changer as long as the chain flows noise suppression → voice changer → output.
CPU cost is real but manageable on modern hardware. Use GPU-offloaded NS if you are tight on CPU headroom. If you genuinely can only run one, let the use case decide: professional communication gets noise suppression; entertainment streaming gets the voice changer, assuming your room is already reasonably quiet.
Download VoxBooster — free 3-day trial, no credit card required, NS built in with correct chain ordering out of the box.