Latency

Latency in voice agents encompasses the entire round-trip time from when a caller speaks to when they hear the agent's response. This includes multiple components: speech recognition (100-300ms), language model processing (200-800ms), and text-to-speech synthesis (100-400ms). Modern voice agents typically achieve end-to-end latencies between 800ms and 2 seconds, though this varies significantly based on infrastructure, model selection, and network conditions. Research by Stanford's Human-Computer Interaction Group found that latencies above 1.5 seconds cause users to perceive conversations as unnatural, while latencies below 700ms are rarely distinguishable from human conversation.

Latency directly determines whether a voice agent feels natural or robotic. Google's research shows that each 100ms increase in latency reduces user satisfaction by 3-5%, with sharp drops after 1.2 seconds. In customer service scenarios, high latency leads to caller frustration, increased handle times, and higher abandonment rates. For sales applications, every second of added latency correlates with a 7% decrease in conversion rates according to Akamai's 2017 study. Voice agents with consistently low latency see 2.3x higher task completion rates and significantly better NPS scores.

Voice agent latency has three main components: First, the audio stream is processed by the Speech-to-Text (STT) service, which converts speech to text in real-time using acoustic and language models. Second, the text is sent to the Large Language Model (LLM) which generates a response based on the conversation context and system prompts. Finally, the Text-to-Speech (TTS) service converts the response back to audio. Each component can be optimized: STT benefits from streaming recognition and voice activity detection, LLM latency improves with smaller models and caching, and TTS speeds up with neural vocoders and pre-computed phoneme mappings. Network optimization through edge deployment and connection pooling can reduce round-trip times by 30-50%.

The most common latency issues stem from cold starts, where the first request takes 3-5x longer due to model loading and connection establishment. Network variability causes inconsistent performance, with mobile networks adding 50-200ms compared to broadband. Model selection presents a trade-off: GPT-4 provides better responses but adds 500-1000ms versus GPT-3.5. Geographic distance to servers can add 100-300ms for transcontinental calls. Many teams overlook the impact of sequential processing - waiting for complete utterances before processing instead of streaming, which can add 500ms+ to perceived latency.

Start by measuring baseline latency across your entire pipeline using tools like OpenTelemetry or custom instrumentation. Set up monitoring for p50, p90, and p99 metrics to understand both typical and worst-case performance. Implement streaming wherever possible - use streaming STT, streaming LLM responses, and chunked TTS playback. Choose models strategically: use faster models for simple queries and reserve larger models for complex requests. Deploy services geographically close to users using CDNs or edge computing. Implement connection pooling and keep-alive to avoid reconnection overhead. Consider pre-computing common responses and using response caching for frequently asked questions. For critical applications, implement fallback strategies when latency exceeds thresholds.