According to a recent LinkedIn post from AssemblyAI, the company is drawing attention to how development teams should handle noisy audio when building voice agents. The post suggests that speech-to-text models are typically trained on real-world noise, so additional noise cancellation on the audio passed to these models may be redundant or even counterproductive.
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Instead, the LinkedIn post highlights that noise cancellation may be most valuable for voice activity detection and turn-taking logic, where cleaner audio can reduce accidental interruptions and improve conversational flow. The recommended approach routes noise‑cancelled audio to voice activity detection systems while sending original, unprocessed audio to the speech-to-text model.
The post also indicates that tuning voice activity detection thresholds can be a cost-free first step before implementing more complex processing. It references a detailed framework from David Lange that examines when noise cancellation can degrade performance, the risk of “sound-alike” artifacts in production, and how to benchmark different configurations.
For investors, this content points to AssemblyAI’s focus on practical, production-oriented guidance for voice AI deployments, which may strengthen its positioning with developer and enterprise customers. By emphasizing optimization strategies around latency, accuracy, and conversational quality, the company could enhance the stickiness of its platform and potentially increase usage-based revenue in real-time voice applications.