AI, Human–Robot Interaction, and Natural Language Processing

Abstract

An AI-driven (or AI-assisted) speech or dialogue system, from an engineering perspective, can be decomposed into a pipeline with a subset of the following three distinct processing activities: (1) Speech processing that turns sampled acoustic sound waves into enriched phonetic information through automatic speech recognition (ASR), and vice versa via text-to-speech (TTS); (2) Natural Language Processing (NLP), which operates at both syntactic and semantic levels to get at the meanings of words as well as of the enriched phonetic information; (3) Dialogue processing which ties both together so that the system can function within the specified latency and semantic constraints. This perspective allows for at least three levels of context. The lowest level is phonetic, where the fundamental components of speech are built from a time-sequence string of acoustic symbols (analyzed in ASR or generated in TTS). The next higher level of context is word- or character-level, normally postulated as sequence-to-sequence modeling. The highest level of context typically used today keeps track of a conversation or topic. An even higher level of context, generally missing today, but which will be essential in future, is that of our beliefs, desires, and intentions.