Natural language understanding (NLU) uses the power of machine learning to convert speech to text and analyze its intent during any interaction.
NLU is a branch of natural language processing (NLP), which helps computers understand and interpret human language by breaking down the elemental pieces of speech. While speech recognition captures spoken language in real-time, transcribes it, and returns text, NLU goes beyond recognition to determine a user’s intent. Speech recognition is powered by statistical machine learning methods which add numeric structure to large datasets. In NLU, machine learning models improve over time as they learn to recognize syntax, context, language patterns, unique definitions, sentiment, and intent.
Business applications often rely on NLU to understand what people are saying in both spoken and written language. This data helps virtual assistants and other applications determine a user’s intent and route them to the right task.
NLU is becoming more widely used for customer communication. This gives customers the choice to use their natural language to navigate menus and collect information, which is faster, easier, and creates a better experience.
Here are some areas where NLU is being used in applications that interact with human language:
Turn nested phone trees into simple “what can I help you with” voice prompts. Analyze answers to “What can I help you with?” and determine the best way to route the call.
Automate data capture to improve lead qualification, support escalations, and find new business opportunities. For example, ask customers questions and capture their answers using Access Service Requests (ASRs) to fill out forms and qualify leads.
Build fully-integrated bots, trained within the context of your business, with the intelligence to understand human language and help customers without human oversight. For example, allow customers to dial into a knowledge base and get the answers they need.