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This special issue of the Journal is devoted to “rethinking” the science of natural language processing (NLP), which serves as the design foundation of spoken dialog systems. Many of the complaints we hear today from speech developers about the rate of performance of automatic speech recognition, and of text to speech synthesis systems, can be effectively addressed by way of a better understanding of natural language processing—an interdisciplinary field of research that combines linguistics, cognitive science, psychology, computer science and artificial intelligence to study the interactions between computers and human (natural) languages. So clarifying NLP is exactly what this special issue aims at doing—in particular, rethinking how NLP can close the gap in the performance of automatic speech recognition and text to speech synthesis systems. To accomplish this, the contributors to Vol. 11, Nos. 3–4 have been at pains to explore, analyze and critique some of the most intricate and complex aspects of NLP—as well as to present novel methodological and practical solutions to some persistent problems that impede the performance of spoken dialog systems. The articles in this issue embrace such tasks as:

Note from the editor: “Rethinking natural language processing for speech technology”