We’ve all interacted with chatbots. “Press 2 for Español.” They’re here; they’re peers; get used to it.

A voice chatbot is an AI-powered persona that interacts with users through voice commands. Examples include Siri, Alexa, Google Assistant, and ChatGPT-4o.

Natural Language Understanding (NLU) and Natural Language Generation (NLG) are the key parts of voice chatbots. NLU helps the chatbot understand what you’re saying. It breaks down your speech to figure out your intent and the important details. NLG is about crafting a response. Once the chatbot understands your input, it uses NLG to create a natural-sounding and relevant reply. 

Voice chatbots function via a series of mechanisms. First, Automatic Speech Recognition (ASR) converts your spoken language into text. This involves the use of acoustic modeling, language modeling, and decoding algorithms to transcribe speech accurately. Natural Language Processing (NLP) interprets the transcribed text. This step involves tokenization, part-of-speech tagging, and dependency parsing to understand user intent and extract relevant informational elements.

The chatbot queries its Knowledge Base (KB) using semantic search and Retrieval Augmented Generation (RAG). Semantic search improves the retrieval of contextually relevant information, while RAG combines KB data with generative models to provide accurate and comprehensive responses.

Intent Recognition and Response Generation employ large language models (LLMs) like BERT or GPT. These models match user intent with the appropriate response, leveraging data from the KB.

Dialog Management maintains conversation context. The dialog manager ensures coherent and context-aware interactions.

Text-to-Speech (TTS) converts text responses into speech. This process involves phoneme generation, prosody modeling, and waveform synthesis to produce natural-sounding speech.

Human beings are, as much as anything else, biological chatbots.