Language pattern analysis for automotive natural language speech applications

Natural language speech user interfaces offer a compelling choice of user interaction for the automotive market. With the increasing number of domains in which speech applications are applied, drivers must currently memorize many command words to control traditional speech interfaces. In contrast, natural language interfaces demand only a basic understanding of the system model instead of memorizing keywords and predefined patterns. To utilize natural language interfaces optimally, designers need to better comprehend how people utter their requests to express their intentions. In this study, we collected a corpus of utterances from users who interacted freely with an automotive natural language speech application. We analyzed the corpus by employing a corpus linguistic technique. As a result, natural language utterances can be classified into three components: information data, context relevant words, and non context relevant vocabulary. Applying this classification, users tended to repeat similar utterance patterns composed from a very limited set of different words. Most of the vocabulary in longer utterances was found to be non context restrictive providing no information. Moreover, users could be distinguished by their language patterns. Finally, this information can be used for the development of natural language speech applications. Some initial ideas are discussed in the paper.