News Monitor (NeMo) is an environment in which the Human Language Technology research unit at FBK brings together its technologies pertaining to Automatic Speech Recognition (ASR), Machine Translation (MT) and Natural Language Processing (NLP). In this view it is a dynamic framework where we can share ideas and technologies, refine algorithms, see and discuss performance and errors of our algorithms that are daily applied on fresh data. In this paper we describe a framework in which a set of parallel news streams in different languages are automatically transcribed and translated. The architecture of the system utilizes modules that perform ASR, MT and NLP. The development of the various modules relies upon a continuous acquisition activity of parallel data (both audio and texts) in different languages. In particular, the availability of large corpora of aligned multilingual text/audio data has allowed to implement unsupervised Acoustic Model (AM) training approaches.
Bibliographic reference. Girardi, C. / Gretter, Roberto / Falavigna, Daniele / Brugnara, Fabio / Giuliani, Diego / Federico, M. (2011): "Nemo: a platform for multilingual news monitoring", In INTERSPEECH-2011, 2261-2264.