Achieving Sub-400ms Voice Latency with Native Multimodal Audio Streams
Traditional voice bots introduce 1.5sā3s of lag due to cascading STT -> LLM -> TTS pipelines. Here is how Lokam bypasses text bottlenecks with native audio-to-audio streaming.
1. The Latency Problem in Conventional Voice Bots
Most voice bots on the market are constructed as "cascaded" pipelines: user audio is recorded, shipped over HTTP to a Speech-to-Text provider, decoded into text, sent to an LLM, streamed back to a Text-to-Speech synthesis service, and finally streamed back to the caller. Each hop adds serialization, network round-trips, and processing jitter, totaling between 1,800ms and 3,200ms. In human conversation, delays greater than 600ms feel robotic and frustrating.
2. The Multimodal Solution: Audio-to-Audio
Lokam solves this by utilizing native bidirectional audio-to-audio neural streaming and optimized real-time media gateways. Raw audio frames are streamed bidirectionally. The neural engine ingests raw sound directly, reasoning about meaning, tone, and inflection without an intermediary text transcription stage. The resulting response is synthesized audio chunks streamed back to the caller in real time.
3. Handling Interruptions & Natural Barge-In
Conversations are fluid. In real dialogues, humans interrupt, ask clarifications, or acknowledge understanding ("uh-huh", "right"). Lokam deploys high-sensitivity Voice Activity Detection (VAD) coupled with low-latency silence watchdogs. The moment user audio energy is detected while the agent is speaking, the agent immediately halts playback and recalculates its conversational trajectory.