Natural Language Understanding engine fused with speech-to-text, beating cloud API accuracy
Rhino Speech-to-Intent infers user intents from utterances, allowing users to interact with applications via voice.
Rhino Speech-to-Intent understands complex voice commands, such as “find the maintenance checklist for Boeing 707” or “call 987 655 4433”.
o = pvrhino.create(access_key,context_path)while not o.process(audio()):passinference = o.get_inference()
Cloud-dependent conventional methods use generic automatic speech recognition (ASR) and natural language understanding (NLU) engines, resulting in subpar accuracy and unreliable response time.
Rhino Speech-to-Intent, fusing ASR and NLU engines, is six times more accurate than Big Tech NLU APIs, enabling elevated user experience and productivity.
Improve productivity with custom voice commands that actually work
Choose the best solution based on data. The open-source natural language understanding benchmark shows that Rhino Speech-to-Intent outperforms cloud conversational AI engines across various accents and in the presence of noise and reverberation.
Build “real” real-time experiences with Rhino Speech-to-Intent. Processing voice commands in the cloud hinders user experience due to fluctuating latency or network performance. Rhino Speech-to-Intent does not send voice commands to a 3rd party cloud and processes them directly on-device.
Ensure user privacy and stay compliant! Rhino Speech-to-Intent processes voice commands locally on the device without recording data and sending them to the cloud. Enterprises can confidently put Rhino Speech-to-Intent in meeting rooms, warehouses, examination rooms, or call centers.
Process voice data on all platforms and offer seamless user experiences. Rhino Speech-to-Intent runs across platforms, including microcontrollers, embedded, mobile, web, on-premise, and cloud.
The best way to see how Rhino Speech-to-Intent differs from other natural language understanding solutions is to try it!Start Now
Natural language understanding focuses on the meaning, i.e., comprehending users’ intent. Researchers initially concentrated on understanding the text and understanding speech is a relatively new field. While spoken language understanding is a more specific term to refer to it, many people, including the industry and researchers, still use natural language understanding for capturing intents from utterances, mainly because the conventional approach is to run speech-to-text and natural language understanding engines subsequently.
Intent Detection is a subtask of natural language processing and a critical component of any task-oriented system. Natural language understanding solutions match users' utterances with one of the predefined classes by understanding the user’s goal (i.e., intention). After matching utterances with intents, the software can initiate a task to achieve users’ goals. For example, users with the intention to turn the lights off may say: “Turn the lights off.”, “Switch off the lights.”, “Can you please turn the lights off?”. Intent detection captures the users’ goal: “change the state of the lights from on to off” despite the different ways to communicate it.
Rhino Speech-to-Intent is a more accurate, resource-efficient, and faster alternative to Amazon Lex, Google DialogFlow, or other NLU engines for use-case-specific intent detection. Picovoice offers a Free Plan to enable experimentation to overcome various challenges. However, if you’re still not sure how to overcome the limitations of Amazon Lex, Google DialogFlow, and other NLU engines with Rhino Speech-to-Intent or need help with migration, leverage Picovoice’s Consulting Services!
Rhino Speech-to-Intent -as the name suggests, converts speech into intent directly without relying on text, obviating the need for automatic speech recognition. Rhino Speech-to-Intent uses the modern end-to-end approach to infer intents and intent details directly from spoken commands, enabling developers to train jointly-optimized automatic speech recognition (ASR) and natural language understanding (NLU) engines for their domain of interest. Rhino Speech-to-Intent specializes in use-case-specific applications, not open-domain applications with billion of spoken command variations. For example, one does not need to discuss the meaning of life with a coffee machine or a surgical robot. Most use cases have a confined domain (context) that covers hundreds or thousands of spoken commands. With use-case-specific and platform-optimized voice AI models, Rhino Speech-to-Intent offers high accuracy with minimal resources.