Leading voice agent developers and providers agree on one core challenge facing the industry: voice AI tends to break down in the real world. Faced with background voices in a bustling café or busy call center, challenged by traffic or changing devices, the acoustic realities of the real world can undermine even the most sophisticated AI pipelines.
The result is missed responses, incorrect responses, turn-taking errors, and a range of other failures that impact upon your voice agent’s performance and brand. To solve the challenge, many voice agents are turning to AI-powered audio enhancement.
But implementing audio enhancement into your voice agent requires consideration and expertise. And a perceived improvement in audio quality doesn’t always mean corresponding growth in ASR performance, which makes it important to choose the right tool.
We should know – it’s what we do every day here at ai-coustics! In this guide, we’ll break down important factors to consider before you invest in audio enhancement.
The core challenge: Suboptimal acoustic environments
Most voice agents break down not in development, but in the real world, when they’re exposed to non-pristine acoustic environments which undermine their performance. The leading pain points include:
- Background voice interference: The biggest issue faced by voice agents is suppressing background voices or secondary speakers, as well as ensuring that background voices aren’t confused with the primary speaker. Too often, background speech leads to interruptions, confused LLMs, and false triggers.
- Varied noise types: Voice agents don’t just face one type of background noise. There’s diverse noise types in the real world, including background traffic, tannoy announcements, public transport, construction sites, household noises like dogs, babies, or family conversations, and much more. This makes it difficult to train a voice agent to simply ‘filter out’ unwanted noise, as there is too much diversity for accurate training.
- Technical device considerations: Audio can be further degraded by low-quality user input. For example, a user might use a speakerphone or microphone that is too far away. Room reverberation also significantly impacts automatic speech recognition (ASR) performance, making audio muddy, so dereverberation capabilities are essential.
- Telephony constraints: Many voice agents operate over low quality phone lines (telephony at 8kHz, often using g711 or mulaw encoding). That means that any noise cancellation solution must also be able to handle frequency spectrum distortions, clipping, and codec compression artifacts common in low-bandwidth audio.
As you can see, voice agents don’t just face one challenge – but a huge spectrum of conflicting issues that lead to poor quality audio. Any audio enhancement solution must be able to address the whole spectrum.
Made for the whole journey: Downstream system integrity
Your voice agent is a pipeline of tools and solutions, and any audio enhancement addition must be evaluated based on its impact on the entire journey, particularly when it comes to ASR accuracy and real-time responsiveness. Your audio enhancement shouldn’t just improve sound – it should improve your product’s whole system performance.
When evaluating audio enhancement solutions, consider:
- ASR performance vs. perceived quality: Better-sounding audio doesn’t always mean a perfectly functioning product. Carefully evaluate whether this higher-quality audio is actually translating into better ASR performance. Some solutions improve perceptual quality but fail to yield better ASR accuracy and can even increase the word error rate (WER).
- Latency requirements: Low latency is critical. Audio enhancement that improves your product performance but takes too long will be frustrating for users and see high turnover. The best voice agents are aiming for an overall per-turn latency under one second (ideally 600–800ms). Additional processing latency should be minimized, ideally in the 5–50ms range or kept around 20ms for streaming applications.
- Voice activity detection (VAD) improvement: Often based on Silero, VAD reliability is a common weak link for voice agents. Your audio enhancement addition should target better VAD performance to solve false triggers caused by background noise. This improves turn detection and prevents voice agents from prematurely stopping or starting speech.
- Transcription accuracy: Audio quality issues lead to major transcription failures, including over-transcription (capturing background voices or announcements), under-transcriptions (missing speech in noisy environments), or low-volume responses (an agent giving only brief answers like “yes” or “no”). Even a small increase in WER accuracy has a massive impact on a user’s perceived performance of your voice agent.
Scaleable by design: Technical and deployment realities
As you integrate audio enhancement into high-volume pipelines, you need to balance compute, compatibility, and cost. Consider:
- Resource constraints: Solutions should have a minimal processing footprint. High CPU usage can lead to buffer overflows which lead to audio outtakes – as though the full audio is stuttering.
- Integration flexibility and runtime: Your audio enhancement solution should integrate smoothly with existing infrastructures and platforms (PipeCat, LiveKit, Vapi, Retell, Layercode, etc.) as well as your languages and interfaces (most common are Python and Node.js). A Rust-based SDK without external dependencies is a particularly useful solution because of it’s ease of integration and reliability.
- Language agnosticism: Voice is global – voice agents should be, too. Whether handling multiple dialects or accents from around the world, your audio enhancement solution should be language-agnostic.
Beyond noise suppression: Features for strategic differentiation
The next frontier isn’t just quieter audio – it’s smarter voice conditioning. And as the voice agent market expands, it’s important to look out for ways to stand out from the competition and ensure a product that goes beyond ‘good’ performance.
- Primary speaker isolation: Ideal for call centers, meeting platforms, and more, this feature distinguishes between the foreground caller and background speakers to isolate the primary speaker’s voice. It separates speakers into individual audio channels, simplifying downstream processing.
- Automated quality testing and monitoring: Too often, companies have relied on ‘vibe tests’ for voice agents and deploying changes to their systems only to encounter strong customer feedback flagging audio failures. To get a step ahead of the game, search for tools which provide automated quality detection and systematic situation capabilities across different devices, noise conditions, and degradation gradients before deployment.
- Adaptive systems: Just as you need audio enhancement that has metrics and quality testing behind it, you also need something reactive and always learning. The ability to dynamically detect audio quality issues will allow your voice agent to adjust confidence thresholds or change its processing strategy in real time.
Built for growth: Business and compliance requirements
Naturally, you’ll need to choose your audio enhancement solution based on non-technical factors including cost structure, testing logistics, and regulatory constraints. Here are some factors beyond performance to consider:
- Pricing model: Startups and smaller businesses might struggle with high upfront costs and prefer monthly subscriptions, simple dollar-based pricing, and pay-as-you-go models over complex token/credit systems. As you grow into an enterprise, pricing models should be able to adapt with you.
- Proof of concept support: Voice agents require rigorous testing and trial periods might be useful to test the solution against its competitors, and in your actual audio and production environment.
- Compliance and data privacy: Your solution should answer to the same stringent compliance and data privacy requirements as your whole product. This is particularly crucial when it comes to regulated industries like finance and healthcare with compliance standards like FedRAMP or HIPAA, which might necessitate on-premise or air-gapped deployment with complete control over data ingress and egress.
The outcome: Voice agents that actually work
Every downstream voice AI task – transcription, recognition, classification, intent parsing – is only as good as its input. A good audio enhancement solution will close the final gap for voice agents between laboratory benchmarks and real-world performance.
Here at ai-coustics, that’s what we’ve been building every day, considering the above factors to provide a well-rounded and high-performing solution for voice agent woes. From voice agent platforms struggling with latency to ASR providers fighting WER degradation and enterprises demanding cost-efficient, compliant infrastructure – ai-coustics is the solution that finally makes voice AI production-ready.
Want to test it out for yourself? Get in touch now or sign up to our developer platform and test it out for yourself.
