Voice AI Echo Cancellation: Causes, Fixes, and Best Practices
Blog post from Coval
Echo cancellation is a critical challenge in voice AI systems, as the technology struggles to differentiate between genuine user input and the system's own text-to-speech (TTS) output, leading to conversational disruptions. Unlike traditional telephony, where echo is merely an auditory annoyance, in voice AI, it can corrupt the input pipeline, causing the agent to respond to itself in a loop. This issue is exacerbated in environments with reflective surfaces or when using devices with poor hardware echo cancellation, such as some Android phones. WebRTC's built-in Acoustic Echo Cancellation (AEC) can mitigate these issues to some extent, but its effectiveness varies across browsers and devices, with Firefox often delivering poorer performance. Architectural solutions like server-side echo cancellation, audio ducking, and barge-in detection with echo awareness can help, though they may introduce latency or limit user interaction capabilities. Testing for echo scenarios is complicated due to the need for physical audio setups, but production monitoring for echo indicators such as conversation loops and high interruption rates can help identify issues. Requiring or detecting headphone usage remains the most reliable method to prevent echo, though it's not always feasible in consumer applications.
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