Bio
Isabel Trancoso received the Licenciado, Mestre, Doutor and Agregado degrees in Electrical and Computer Engineering from Instituto Superior Técnico, Lisbon, Portugal, in 1979, 1984, 1987 and 2002, respectively. She has been a lecturer at this University since 1979, having coordinated the EEC course for 6 years. She is currently a Full Professor, teaching speech processing courses. She is also a senior researcher at INESC ID Lisbon, having launched the speech processing group, now restructured as L2F, in 1990. Her first research topic was medium-to-low bit rate speech coding. From October 1984 through June 1985, she worked on this topic at AT&T Bell Laboratories, Murray Hill, New Jersey. Her current scope is much broader, encompassing many areas of spoken language processing, with a special emphasis on tools and resources for the Portuguese language. She was a member of the ISCA (International Speech Communication Association) Board (1993-1998), the IEEE Speech Technical Committee, and the Permanent Council for the Organization of the International Conferences on Spoken Language Processing. She was elected Editor in Chief of the IEEE Transactions on Speech and Audio Processing (2003-2005), Member-at-Large of the IEEE Signal Processing Society Board of Governors (2006-2008), Vice-President of ISCA (2005- 2007) and President of ISCA (2007-2011). She chaired the Organizing Committee of the INTERSPEECH’2005 Conference in Lisbon. She received the 2009 IEEE Signal Processing Society Meritorious Service Award, and was elevated to IEEE Fellow in 2011.

HLT4LL in Portuguese
The use of human language technologies for language learning is not new, but the maturity of these technologies coupled with advances in multimedia processing, and the wide use of internet open up many interesting opportunities. Our first efforts in the language learning area concerned the development of a Portuguese version of a tutoring system (REAP) from Carnegie Mellon University, focused in vocabulary learning. In the baseline version, students can learn from real texts selected from an open corpus such as the Web, on topics for which they previously marked their preference. One of the major innovations was the use of other documents beyond text, such as automatically aligned audiobooks or automatically recognized TV documentaries. In fact, our Daily REAP version is updated every day to allow students to learn from the written or broadcast news of the last 7-days, on the topics they choose. This version does in fact use all the different technologies integrated in our broadcast news processing chain, starting with audio segmentation and speech recognition (marking the words recognized with lower confidence), and including as well capitalization, punctuation, story segmentation, and topic indexation. These technologies also enabled the automatic selection of appropriate sentences for vocabulary perception exercises, which are particularly important for languages such as European Portuguese, characterized by strong vowel reduction that makes it more difficult for non-native learners. Word naming exercises are also a good illustration of the use of LT, not only for language learning, for also for speech therapy. A keyword spotting module is integrated in a very flexible platform that allows the teacher, or the therapist, to create new exercises, by uploading pictures, and video or audio segments, and corresponding answers. One of the main directions for our language learning efforts is the area of serious games. Our continuously growing set of games targets totally different goals such as learning grammar, or practicing vocabulary, just to name a few. Practically every NLP or speech technology module available at our lab has found an application in these games, from statistical machine translation to speech synthesis and recognition, integrating 3D technologies as well to make the games more appealing. In fact, gamification is pervasively penetrating into many areas of learning, hence finding more challenges for human language technologies.