Guest Editors Introduction: Machine Learning in Speech and Language Technologies
Machine learning techniques have long been the foundations of speech processing. Bayesian classification, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the
... oisy channel model for machine translation have roots in work conducted in the speech field. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to significant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classifiers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between speech and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected reflecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of machine learning and from that of the speech and natural language applications perspective. The papers in this special issue cover a broad range of topics in natural language and spoken language processing as well as in machine learning. From both perspectives the selection reflects the maturity of the field which has moved to address harder problems using more sophisticated techniques.