Your Audio Quality Might Be Breaking Your Voice Agents

This post was adapted from Hamming’s podcast conversation with Fabian Seipel, Co-founder of ai-coustics. ai-coustics is building the quality layer for Voice AI, transforming any audio input into clear, production-ready sound.

Most discussions about voice AI focus on models, latency, or prompt engineering. But there is an overlooked issue that can undermine voice agent reliability: the quality of the input audio reaching the ASR.

Before an agent ever interprets a user request, raw audio has to survive the full ASR front-end pipeline — room acoustics, microphone limitations, digitization, compression, and transmission. By the time the system receives the waveform, it has already been shaped by dozens of variables outside the developer’s control.

In a recent conversation of The Voice Loop podcast with Fabian Seipel, co-founder of ai-coustics, we explored why input audio remains one of the biggest production bottlenecks in voice AI, and why fixing it requires both better enhancement models and more comprehensive testing.

How Audio Degrades Through the Pipeline

Audio degrades across the entire signal chain. Room acoustics and background noise immediately shape the signal. The sound that reaches a microphone adds its own coloration and noise profile. The device then digitizes the signal, where clipping, codec artifacts, and compression can alter it further. Transmission systems may re-encode or resample the audio before it finally reaches the ASR.

Each stage introduces variation; two clips that sound similar to a human can look drastically different to a model, producing transcription errors that cascade downstream. This is why improving ASR accuracy requires not just better models, but better input quality.

Why Noise Is Only Part of the Problem

Teams often frame audio issues as “noise problems,” but noise is just one category. Fabian highlights a wider set of degradations that routinely affect voice agents:

• Room reverberation and reflections
• Users standing far from microphones or using speakerphone
• Differences in device frequency response and sampling rates
• Competing human speakers and cross-talk
• Digital clipping or oversaturation
• Compression artifacts from telephony or VoIP
• Distortions introduced during transmission

How ai-coustics Approaches Enhancement

ai-coustics focuses on improving the audio before it reaches the ASR. Their pipeline blends digital signal processing (DSP) and ML techniques, with many of their models operating on spectrograms rather than waveforms. This allows them to leverage techniques adapted from computer vision.

ai-coustics’ method consists of four steps:

1. High-quality input collection: ai-coustics collects diverse clean speech across accents, languages, intonations, speech patterns, and acoustic characteristics.
2. Controlled degradation: They simulate real-world impairments — noise, reverb, clipping, compression, distance effects, and device coloration — to create degraded versions of clean audio.
3. ML-based restoration: Models learn to remove or reconstruct missing information. Some models take a subtractive approach, while others rebuild the audio in a generative way.
4. Continuous expansion: As new edge cases appear, the team expands the set of degradations in the simulation pipeline.

The Hardest Environments for Voice Agents

Some environments consistently challenge even strong ASR and enhancement pipelines. Fabian noted the hardest environments for voice agents:

• Drive-through ordering/QSR: Engines, outdoor noise, and distance from the speaker create low-quality, reverberant input that is difficult for ASR systems to interpret.
• Outbound calls to noisy environments: Factories, warehouses, construction sites, transport hubs, and similar locations introduce non-stationary noise patterns that interfere with VAD and degrade intelligibility.
• Repair shops and service centers: Tools and machinery create irregular acoustic bursts that models often misinterpret as speech.
• Contact centers: Crosstalking from nearby agents can trigger interruptions or false activations.

Building the Case for Standards in Voice AI: Starting with Audio Quality

Every team evaluates audio differently. Some look at intelligibility, some focus on noise resilience, and others on device variability or VAD stability. Yet the industry has no shared definition of what “production-ready” audio actually means. That gap becomes visible the moment a system moves beyond controlled demos and encounters the full range of real-world conditions.

A standard for audio quality would create a more reliable foundation. It could define how agents should perform across representative acoustic scenarios, how input variability should be handled, and what thresholds ASR systems must meet when audio is degraded, compressed, or captured through low-fidelity hardware. In practice, this is the layer where most of the unpredictable behavior originates, and where consistency would have the greatest downstream impact.

The idea of a formal benchmark is also becoming more realistic. Audio enhancement models are now strong enough to stabilize inputs across devices and environments, and ASR systems are improving as their training data expands to cover more languages, speaking styles, and prosodic variation.

What’s Next?

As audio enhancement and ASR systems continue to improve, the gap between ideal testing conditions and real-world usage will narrow. Better input quality will expand where voice agents can operate reliably and make it easier for enterprises to deploy them at scale. Over time, these advances will support clearer benchmarks for audio and strengthen the foundations of production-grade voice AI.

Listen to the full conversation on The Voice Loop.