Intent detection engine fuses Natural Language Understanding with Speech-to-Text, outperforming cloud APIs
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 translate voice to text using generic automatic speech recognition (ASR), then detect user intent by analyzing text using natural language understanding (NLU). Processing voice data in two phases decreases accuracy and increases latency.
Rhino Speech-to-Intent, fusing ASR and NLU engines, does not rely on text representation to infer user intent, achieving six times higher accurate than Big Tech NLU APIs and enabling elevated user experiences.
Improve productivity with custom voice commands that actually work
The best way to see how Rhino Speech-to-Intent differs from other natural language understanding solutions is to try it!
Start NowNatural language understanding deals with meaning, i.e., comprehending users’ intent. Researchers initially started with understanding user intents from the text. While spoken language understanding is a more specific term to refer to understanding user intent from speech, many people, including the industry and researchers, still use natural language understanding for both text and speech data. This is mainly due to the conventional approach of running 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, eliminating the need for text representation. Rhino Speech-to-Intent uses the modern end-to-end approach to infer intents and intent details directly from spoken commands. This enables developers to train jointly optimized automatic speech recognition (ASR) and natural language understanding (NLU) engines tailored to their specific domain, achieving higher accuracy.
Rhino Speech-to-Intent excels in use-case-specific applications, such as voice-enabled coffee machines or surgical robots, which involve a limited number of commands, offering high accuracy with minimal resources. In contrast, open-domain applications like voice-enabled ChatGPT handle a wide range of topics and variations. Thus, we recommend Cheetah Streaming Speech-to-Text and picoLLM for such applications.