As users become more accustomed to using their mobile devices to organize and schedule their lives, there is more of a demand for applications that can make that process easier. Automatic speech recognition technology has already been developed to enable essentially unlimited vocabulary in a mobile setting. Understanding the words that are spoken is the next challenge. In this paper, we describe efforts to develop a dataset and classifier to recognize named entities in speech. Using sets of both real and simulated data, in conjunction with a very large set of real named entities, we created a challenging corpus of training and test data. We developed a multi-stage framework to parse these utterances and simultaneously tag names and locations. Our combined system achieved an f-measure of 0.87 on extracted proper nouns, with a 95% accuracy on distinguishing names from locations.
Bibliographic reference. Polifroni, Joseph / Seneff, Stephanie (2010): "Combining word-based features, statistical language models, and parsing for named entity recognition", In INTERSPEECH-2010, 1289-1292.