This workshop is intended for researchers interested in machine learning methods for speech and language processing and in unifying approaches to several outstanding speech and language processing issues. In the last few years, significant progress has been made in both research and commercial applications of speech and language processing. Despite the superior empirical results, however, there remain important theoretical issues to be addressed. Theoretical advancement is expected to drive greater system performance improvement, which in turn generates the new need of in-depth studies of emerging novel learning and modeling methodologies. The main goal of the proposed workshop is to fill in the above need, with the main focus on the fundamental issues of new emerging approaches and empirical applications in speech and language processing. Another focus of this workshop is on the unification of learning approaches to speech and language processing problems. Many problems in speech processing and in language processing share a wide range of similarities (despite conspicuous differences), and techniques in speech and language processing fields can be successfully cross-fertilized. It is of great interest to study unifying modeling and learning approaches across these two fields. In summary, we hope that this workshop will present an opportunity for intensive discussions of emerging learning methods among speech processing, language processing, and machine learning researchers, and will inspire unifying approaches to problems across the speech and language processing fields.