Accelerating the adoption of voice AI
through innovation
Cloud vs Edge deployment of voice AI:
trade-offs in performance, privacy, and cost
| TYPICAL EDGE STACK | PICOVOICE STACK | |
|---|---|---|
| ⚪ DATA | 3rd-party / open datasets ⚠️ No or limited visibility and quality control | Proprietary pipelines & custom curation 👍 Ensures diversity, fairness, edge-optimization |
| ⚪ MODEL | Cloud-pretrained (e.g., Whisper) ⚠️ Retrofitted, not edge-native | Edge-first proprietary training framework 👍 Efficiency built in from the start |
| ⚪ RUNTIME | Generic Runtimes (e.g., PyTorch, Onnx) ⚠️ No access to core tech for full optimization | Proprietary inference engine 👍 Memory & compute optimized, zero dependencies |
| ⚪ OPTIMIZE | Post-training and development ⚠️ Restricted scope, performance trade-offs | Full-stack control 👍 End-to-end optimization at every layer |
| ⚫ RESULT | ❌ Trade-off: accuracy vs. resource utilization ❌ Cannot match cloud-level accuracy ❌ Introduce compute latency | ✅ Cloud-level accuracy with no compromises ✅ Low latency ✅ Reliable real-time operation |
Learn about real Picovoice impacts
Building & choosing the best voice AI technology
Voice AI is a complex and rapidly evolving technology. Vendors' claims like "the best," "revolutionary," and "most accurate" often fail to help enterprises make informed decisions. Recognizing the lack of scientific methods for choosing the best wake word engine, we developed an open-source wake word benchmark. Addressing a real need led to its adoption by the researchers in the industry and academia. As we introduced new products, we open-sourced our internal benchmarks, which were originally used to ensure that Picovoice's voice AI technology is always on par with — or better than — cloud-dependent voice AI APIs.
Open-source text-to-speech latency benchmark compares the response times of different voice generators when used in LLM-based voice assistants. [Amazon Polly, Azure Text-to-Speech, ElevenLabs, OpenAI TTS, Picovoice Orca Text-to-Speech]
Open-source speech-to-text benchmark is a scalable framework to compare Amazon Transcribe, Azure Speech-to-Text, Google Speech-to-Text, IBM Watson Speech-to-Text, OpenAI Whisper Speech-to-Text, Cheetah Streaming Speech-to-Text, and Picovoice Leopard Speech-to-Text.
Open-source speech enhancement and noise suppression comparison brings a scientific, transparent, and objective framework to compare noise cancellation solutions. [Mozilla RNNoise Noise Suppression, Koala Noise Suppression]
Open-source speaker diarization comparison compares speaker diarization capabilities of Amazon Transcribe Speaker Diarization, Azure Speech-to-Text Speaker Diarization, Google Speech-to-Text Speaker Diarization with Falcon Speaker Diarization and pyannote Speaker Diarization.
Open-source speaker recognition comparison enables data-driven decision making while choosing the best speaker verification and identification SDK. [pyannote Speaker Recognition, SpeechBrain Speaker Recognition, Eagle Speaker Recognition]
Open-source wake word benchmark evaluates the performance of freely available wake word detection engines. Enterprises can add other alternatives to the comparison framework. [PocketSphinx Wake Word, Snowboy Wake Word, Porcupine Wake Word]
Open-source natural language understanding benchmark is a scalable framework to compare the voice command acceptance performance of Amazon Lex, Google Dialogflow, IBM Watson, Microsoft LUIS, and Picovoice Rhino Speech-to-Intent.
On-device Voice AI Offerings
Each Picovoice offering has a unique advantage, creating new opportunities for enterprises to bring their vision to life.
Products
Services
Tools
Picovoice Voice Recorders eliminates one of the biggest problems in voice AI: audio processing.
Voice AI engines receive audio streams and process them to generate the desired output. Voice AI vendors focus on processing the audio streams. Creating audio streams is a challenge left to developers. Especially finding a solution for real-time audio processing blocks many developers.
We initially built voice recorders for Picovoice engines to simplify the development process. Acknowledging the challenges, we created separate libraries, enabling developers to use them freely.
Incorporating audio output capabilities into your software can be a challenging endeavour. Most developers have limited experience with digital audio beyond voice assistant apps or other audio file playback options. To make matters worse, audio library usability and platform support can vary significantly based on what framework you're working with.
To make life easier for developers, we have created a collection of open-source SDKs designed to streamline audio processing and output, making them as straightforward as possible.