Improving Conversational AI Latency Through an Efficient TTS Pipeline

For conversational AI to feel natural, responses must be immediate. When latency occurs, the rhythm of the interaction breaks down and the user experience deteriorates. Therefore, optimizing the TTS (Text-to-Speech) pipeline to shorten response time is key.

The main technical strategies for reducing latency are as follows.

• Optimizing model complexity and inference speed: High-performance models like Tacotron or WaveNet offer high quality but require heavy computation, which can cause delays. To address this, models are made lighter or Quantization techniques are applied to reduce computational load.
• Audio streaming: Using Streaming TTS, which transmits audio as soon as it is generated rather than waiting for the entire sentence to be synthesized, enables real-time conversation.
• Preloading and caching: Frequently used phrases can be pre-synthesized and stored through Preloading, enabling immediate responses.
• Edge Computing: By performing Local Inference directly on the user's device without going through a cloud server, network latency is minimized.