Abstract: Three dimensional [3D] image data and auxiliary graphical data are combined for rendering on a 3D display (30) by detecting depth values occurring in the 3D image data, and setting auxiliary depth values for the auxiliary graphical data (31) adaptively in dependence of the detected depth values. The 3D image data and the auxiliary graphical data at the auxiliary depth value are combined based on the depth values of the 3D image data. First an area of attention (32) in the 3D image data is detected. A depth pattern for the area of attention is determined, and the auxiliary depth values are set in dependence of the depth pattern.

Abstract: Three dimensional [3D] image data and auxiliary graphical data are combined for rendering on a 3D display (30) by detecting depth values occurring in the 3D image data, and setting auxiliary depth values for the auxiliary graphical data (31) adaptively in dependence of the detected depth values. The 3D image data and the auxiliary graphical data at the auxiliary depth value are combined based on the depth values of the 3D image data. First an area of attention (32) in the 3D image data is detected. A depth pattern for the area of attention is determined, and the auxiliary depth values are set in dependence of the depth pattern.

Abstract: Three dimensional [3D] image data and auxiliary graphical data are combined for rendering on a 3D display by detecting depth values occurring in the 3D image data and setting auxiliary depth values for the auxiliary graphical data adaptively in dependence of the detected depth values. The 3D image data and the auxiliary graphical data at the auxiliary depth value are combined based on the depth values of the 3D image data. First an area of attention in the 3D image data is detected. A depth pattern for the area of attention is determined, and the auxiliary depth values are set in dependence of the depth pattern.

Abstract: Three dimensional [3D] image data and auxiliary graphical data are combined for rendering on a 3D display (30) by detecting depth values occurring in the 3D image data, and setting auxiliary depth values for the auxiliary graphical data (31) adaptively in dependence of the detected depth values. The 3D image data and the auxiliary graphical data at the auxiliary depth value are combined based on the depth values of the 3D image data. First an area of attention (32) in the 3D image data is detected. A depth pattern for the area of attention is determined, and the auxiliary depth values are set in dependence of the depth pattern.

Abstract: The invention relates to a device and method of providing a layered depth model of a scene, the layers of the depth model comprising primary view information for a primary view of the scene from a primary viewing direction and occlusion information associated with the primary view information for use in rendering in other views of the scene, wherein: the primary view information comprises layer segments of the model which are depth-wise closest with respect to the primary viewing direction, and the occlusion information comprises further layer segments of the model and wherein the occlusion in formation comprises a safety region (SR1, SR2, SR3, SR4) adjacent to a depth transition for which occlusion information is provided (J1, J2, J3, J4), and wherein the safety region comprises corresponding segments of the primary view information, and wherein the safety region (SR1, SR2, SR3, SR4) is located on that side of the respective depth transition which is depth-wise farthest away with respect to the primary viewi

Abstract: A method of improving speech recognition includes receiving a plurality of recognized sentences from an automatic speech recognition (ASR) engine, each of the plurality of the recognized sentences including a plurality of information items. A confirmation is also receive that a value of an information item of the plurality of information items has been misrecognized or recognized. If the value has been misrecognized, each of the plurality of sentences are deleted that include the value of the information item that was misrecognized. If the value has been recognized, each of the plurality of sentences that do not include the value of the information item is deleted. The remaining sentences are transmitted to a dialog manager module.

Abstract: A method for improving speech recognition accuracy includes utilizing skiplists or lists of values that cannot occur because of improbability or impossibility. A table or list is stored in a dialog manager module. The table includes a plurality of information items and a corresponding list of improbable values for each of the plurality of information items. A plurality of recognized ordered interpretations is received from an automatic speech recognition (ASR) engine. Each of the plurality of recognized ordered interpretations each includes a number of information items. A value of one or more of the received information items for a first recognized ordered interpretation is compared to a table to determine if the value of the one of the received information items matches any of the list of improbable values for the corresponding information item.

Abstract: A device improves speech recognition accuracy by utilizing an external knowledge source. The device receives a speech recognition result from an automatic speech recognition (ASR) engine, the speech recognition result including a plurality of ordered interpretations, wherein each of the ordered interpretations includes a plurality of information items. The device analyzes and filters the plurality of interpretations using an external knowledge source to create a filtered plurality of ordered interpretations. The device stores the filtered plurality of ordered interpretations to a memory. The device transmits the filtered plurality of ordered interpretations to a dialog manager module to create a textual output. Alternatively, the dialog manager module retrieves the filtered plurality of ordered interpretations from a memory.

Abstract: A method for improving speech recognition accuracy includes utilizing skiplists or lists of values that cannot occur because of improbability or impossibility. A table or list is stored in a dialog manager module. The table includes a plurality of information items and a corresponding list of improbable values for each of the plurality of information items. A plurality of recognized ordered interpretations is received from an automatic speech recognition (ASR) engine. Each of the plurality of recognized ordered interpretations each includes a number of information items. A value of one or more of the received information items for a first recognized ordered interpretation is compared to a table to determine if the value of the one of the received information items matches any of the list of improbable values for the corresponding information item.

Abstract: A device improves speech recognition accuracy by utilizing an external knowledge source. The device receives a speech recognition result from an automatic speech recognition (ASR) engine, the speech recognition result including a plurality of ordered interpretations, wherein each of the ordered interpretations includes a plurality of information items. The device analyzes and filters the plurality of interpretations using an external knowledge source to create a filtered plurality of ordered interpretations. The device stores the filtered plurality of ordered interpretations to a memory. The device transmits the filtered plurality of ordered interpretations to a dialog manager module to create a textual output. Alternatively, the dialog manager module retrieves the filtered plurality of ordered interpretations from a memory.

Abstract: A method of improving speech recognition includes receiving a plurality of recognized sentences from an automatic speech recognition (ASR) engine, each of the plurality of the recognized sentences including a plurality of information items. A confirmation is also receive that a value of an information item of the plurality of information items has been misrecognized or recognized. If the value has been misrecognized, each of the plurality of sentences are deleted that include the value of the information item that was misrecognized. If the value has been recognized, each of the plurality of sentences that do not include the value of the information item is deleted. The remaining sentences are transmitted to a dialog manager module.

Abstract: A method is described for multi-user operation of a barge-in dialogue system (1). The dialogue system comprises a front-end computer unit (2) with a plurality of access channels (6) for the users and a plurality of servers (18, 19, 20, 21) having each a number of speech processing units (22). Each of the speech processing units (22) comprises a speech activity detector (23) and a speech recognition unit (24). During a dialogue between the system and a user, a new speech processing unit (22) is repeatedly assigned at various specific times to the user-deployed access channel (6) so as to achieve as uniform a utilization of the servers (18, 19, 20, 21) as possible. The speech activity detector (23) detects an in-coming speech signal on the access channel (6) to which channel the speech processing unit (22) is assigned at this time, and activates the speech recognition unit (24). It addition, a corresponding barge-in dialogue system (1) is described.