Time: 1245pm-145pm, Friday, April 16 Place: Room 6496, CUNY Graduate Center, 365 Fifth Ave (34str&35str). Speaker: Sameer Maskey (IBM) Title : Power Mean Based Algorithm for Combining Alignments in Speech-to-Speech Translation Abstract: Speech-to-Speech (S2S) translation system can improve communication among speakers who do not share a common language by translating bi-directional speech in real time. Although advances made in Speech-to-Speech (S2S) translation systems over the last decade have made it possible to deploy real time S2S systems for certain domains and languages, human-level accuracy is far from being achieved. In this talk, I will describe some of the research problems we face when we develop S2S systems for low resource languages such as Dari and Pashto. Particularly, I will focus on Machine Translation (MT) component of S2S system and describe the problem of alignment combination. Combining alignments based on direction of translation have shown to be useful for MT models; but most of the current combination methods are based on heuristics. I will present a mathematical formulation for combining an arbitrary number of alignment tables using their power mean that does not rely on heuristics. The method frames the combination task as an optimization problem, and finds the optimal alignment lying between the intersection and union of multiple alignment tables by optimizing the parameter p: real number defining the order of the power mean function. I will describe how this combination method results in better S2S system for English-Pashto language pair. Bio: Sameer Maskey is a Research Staff Member at IBM T.J Watson Research Center in Yorktown Heights, New York. He is also teaching this semester at Columbia University as an Adjunct Assistant Professor in the Department of Computer Science. He received his Ph.D. in Computer Science from Columbia University in 2008. He got his undergraduate degree (Honors) in 2002 from Bates college in Mathematics and Physics. His main research interests are Machine Learning/Statistical Techniques for Natural Language and Speech processing, particularly Machine Translation and Summarization of spoken documents. He has previously worked on other topics such as Information Extraction, Speech Synthesis and Question Answering. Currently, he is developing statistical methods to improve various aspects of speech-to-speech translation for low resource languages.