Inner speech is an aspect of human cognition that has been largely neglected by traditional artificial
intelligence research. It is argued here that inner speech is an important contributor to cognition and
consciousness and therefore also conscious machines should incorporate it. The realization of inner
speech in machines involves also notoriously difficult linguistic issues, like sentence understanding.
Here an approach to language processing by associative neural networks is proposed as the solution.
This method works without explicit parsing or grammatical rules. The cognitive effects of inner
speech arise from its content; inner speech is about something and that content affects the operation
and behavior of the cognitive system. Consciousness involves the awareness of the mental content;
inner speech is seen here as one tool for introspection that facilitates this awareness. In inner speech
we may comment ourselves in a way that we have learned from others. This self-appraisal is seen as
a process that leads to enhanced social self-awareness and self-image.