Improve Voice Assistant Accuracy and Latency
Voice assistants built on cloud services, using Amazon Lex, Google Dialogflow or Gemini, Cerence CaLLM, Microsoft Voice Live API, OpenAI Agents SDK transcribe speech and interpret it in two separate steps, an approach that compounds errors. If it's a closed pipeline like Microsoft Voice Live API, you don't see where the error occurs, i.e., you can't see if it's a transcription or language model error. They include no built-in wake word, add network latency on every turn, and send user audio to third parties.
Picovoice's on-device voice AI stack lets developers build hands-free, accurate, low-latency custom voice assistants.
Why Cloud Voice Agents Struggle with Accuracy and Latency?
A voice agent or assistant has to detect a wake word, understand the request, and respond. Cloud stacks generally split understanding into two stages, transcription and then natural language understanding, and route each stage over the network. That architecture creates five problems:
Custom Voice AI Agents and Assistants That Run On-Device
Picovoice runs the voice AI agents and assistants on the device, whether they're for specialized platforms, such as embedded voice assistant, or tasks like maintenance and inspection, running LLMs, performing access control via speaker recognition. Each stage is local, so the round trips disappear.
Higher Accuracy with Purpose-Built Solutions and Models
Running large models, such as LLMs, for everything is inefficient and may have significant accuracy and performance implications. It's like driving a Ferrari on every occasion including pizza delivery.
Using purpose-built solutions, such as Rhino Speech-to-Intent and custom models, such as transcription models fine-tuned for the medical domain result in higher accuracy and lower latency.
For example, Rhino, a deterministic intent recognition engine, maps speech directly to intent. Since there is no transcript, speech recognition errors do not accumulate. Since it's deterministic, it doesn't hallucinate. In the open-source NLU benchmark, Rhino resolves requests at 97.3% acceptance, making 6x fewer errors than the Big Tech average.
Real-Time Responses
Because the pipeline is local and lightweight voice agents and assistants answer without waiting on the network. For example, Orca Streaming Text-to-Speech starts reading LLM responses in 128 ms after the first token is generated, 2.6x faster than ElevenLabs streaming, so the reply begins almost immediately instead of after a cloud round trip.
Private and Offline by Design
On-device processing means microphone audio is never transmitted, logged, or retained, and the assistant keeps working with no connectivity. That is what makes it viable for healthcare, finance, automotive, and field deployments, and it is GDPR, HIPAA, and CCPA compliant by design rather than by policy. And it is cost-effective at scale, without the unbounded cloud costs that grow with usage.
When Cloud Voice Agents and Assistants Fit Better?
Cloud voice agents and assistants can be the simpler choice for low-volume projects with no latency or privacy constraints, such as prototypes.
Picovoice is the right choice when accuracy, latency, privacy, offline operation, or cost at scale decide whether the product ships.