Abstract: A method for determining a set of distortion measures in a pattern recognition process, where a sequence of feature vectors is formed from a digitized incoming signal to be recognized, said pattern recognition being based upon said set of distortion measures. The method comprises comparing (S10) a first feature vector in said sequence with a first number (M1) of templates from a set of templates representing candidate patterns, based on said comparison, selecting (S12) a second number (M2) of templates from said template set, the second number being smaller than the first number, and comparing (S14) a second feature vector only with said selected templates. The method can be implemented in a device for pattern recognition.

Abstract: A user interface provides for contextual navigation in locating desired content based on similarity/dissimilarity criteria. Each item that is identified in a device is provided with at least one multi-dimensional descriptor. A content of each item can be stored remotely from the device. A search criteria is selected that relates to the descriptor and a selected active item. A search is conducted to identify all other items identified in the device that have a relationship with the search criteria. The results are presented to the user and can be ranked according to a selected relationship order.

Abstract: Pattern recognition, wherein a sequence of feature vectors is formed from a digitized incoming signal, the feature vectors comprising feature vector components, and at least one feature vector is compared with templates of candidate patterns by computing a distortion measure. A control signal based on at least one time-dependent variable of the recognition process is formulated, and the distortion measure is computed using only a subset of the vector components of the feature vector, the subset being chosen in accordance with said control signal. This reduces the computational complexity of the computation, as the dimensionality of the vectors involved in the computation is effectively reduced. Although such a dimension reduction decreases the computational need, it has been found not to significantly impair the classification performance.

Abstract: A mobile station including an illumination section for emitting light, a display section upon which an image can be formed and through which light can pass, and a display controller for controlling the image wherein the display section, in a first position, is generally parallel to and at least partially overlaps the illumination section and wherein the display section, in a second position, is separated from the illumination section into a non-overlapping position.

Abstract: A speech recognition system comprising: a language identification unit for identifying the language of a text item entry; at least one separate pronunciation modelling unit including a phoneme set and pronunciation model for at least one language; means for activating the pronunciation modelling unit including the phoneme set and pronunciation model for the language corresponding to the language identified in the language identification unit for obtaining a phoneme transcription for the entry; and a multilingual acoustic modelling unit for creating a recognition model for the entry.

Abstract: Pattern recognition, wherein a sequence of feature vectors is formed from a digitized incoming signal, the feature vectors comprising feature vector components, and at least one feature vector is compared with templates of candidate patterns by computing a distortion measure.

Abstract: A method for determining a set of distortion measures in a pattern recognition process, where a sequence of feature vectors is formed from a digitized incoming signal to be recognized, said pattern recognition being based upon said set of distortion measures. The method comprises comparing (S10) a first feature vector in said sequence with a first number (M1) of templates from a set of templates representing candidate patterns, based on said comparison, selecting (S12) a second number (M2) of templates from said template set, the second number being smaller than the first number, and comparing (S14) a second feature vector only with said selected templates. The method can be implemented in a device for pattern recognition.