If you talk to a man in a language he understands, that goes to his head. If you talk to him in his language, that goes to his heart. I would venture to guess that most people had their first encounter with natural language processing (NLP) when Apple added Siri to the iPhone. Starting with the iPhone 4S, you could ask "her" simple questions such as "Who was the 12th president of the United States?" (Zachary Taylor) and "Will you marry me?" (We hardly know one another). Personally, I use Siri on a near daily basis for getting me to where I need to go and finding the best Indian, Thai, or Mediterranean restaurant once I arrive there.

In this work, a methodology is developed to detect sentient actors in spoken stories. Meta-tags are then saved to XML files associated with the audio files. A recursive approach is used to find actor candidates and features which are then classified using machine learning approaches. Results of the study indicate that the methodology performed well on a narrative based corpus of children’s stories. Using Support Vector Machines for classification, an F-measure accuracy score of 86% was achieved for both named and unnamed entities. Additionally, feature analysis indicated that speech features were very useful when detecting unnamed actors.