In this paper, an adaptive activity and environment recognition algorithm running on a mobile phone is presented. The algorithm makes inferences based on sensor and radio receiver data provided by the phone. A wide set of features that can be extracted from these data sources were investigated, and a Bayesian maximum a posteriori classifier was used for classifying between several user activities and environments. The accuracy of the method was evaluated on a dataset collected in a real-life trial. In addition, comparison to other state-of-the-art classifiers, namely support vector machines and decision trees, was performed. To make the system adaptive for individual user characteristics, an adaptation algorithm for context model parameters was designed. Moreover, a confidence measure for the classification correctness was designed. The proposed adaptation algorithm and confidence measure were evaluated on a second dataset obtained from another real-life trial, where the users were requested to provide binary feedback on the classification correctness. The results show that the proposed adaptation algorithm is effective at improving the classification accuracy.

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Dictation of natural language text on embedded mobile devices is a challenging task. First, it involves memory and CPU-efficient implementation of robust speech recognition algorithms that are generally resource demanding. Secondly, the acoustic and language models employed in the recognizer require the availability of suitable text and speech language resources, typically for a wide set of languages. Thirdly, a proper design of the UI is also essential. The UI has to provide intuitive and easy means for dictation and error correction, and must be suitable for a mobile usage scenario. In this demonstrator, an embedded speech recognition system for short message (SMS) dictation in US English is presented. The system is running on Nokia Series 60 mobile phones (e.g., N70, E60). The system's vocabulary is 23 thousand words. Its Flash and RAM memory footprints are small, 2 and 2.5 megabytes, respectively. After a short enrollment session, most native speakers can achieve a word accuracy of over 90% when dictating short messages in quiet or moderately noisy environments.

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