Abstract: An automatic speech recognition system recognizes user changes to dictated text and infers whether such changes result from the user changing his/her mind, or whether such changes are a result of a recognition error. If a recognition error is detected, the system uses the type of user correction to modify itself to reduce the chance that such recognition error will occur again. Accordingly, the system and methods provide for significant speech recognition learning with little or no additional user interaction.

Abstract: An automatic speech recognition system recognizes user changes to dictated text and infers whether such changes result from the user changing his/her mind, or whether such changes are a result of a recognition error. If a recognition error is detected, the system uses the type of user correction to modify itself to reduce the chance that such recognition error will occur again. Accordingly, the system and methods provide for significant speech recognition learning with little or no additional user interaction.

Abstract: An automatic speech recognition system recognizes user changes to dictated text and infers whether such changes result from the user changing his/her mind, or whether such changes are a result of a recognition error. If a recognition error is detected, the system uses the type of user correction to modify itself to reduce the chance that such recognition error will occur again. Accordingly, the system and methods provide for significant speech recognition learning with little or no additional user interaction.

Abstract: A method for responding to an electronic mail message with a limited input device such as a phone includes audibly rendering the question and a set of proposed answers typically provided in the electronic mail message by the sender of the electronic mail message. A language model indicative of the proposed answers is provided to a speech recognizer. The response from the user is obtained and converted to a textual response using the speech recognizer and language model. A second electronic e-mail message is then sent back to the sender. The second electronic mail message includes the textual response.

Abstract: The method and apparatus utilize a filter to remove a variety of non-dictated words from data based on probability and improve the effectiveness of creating a language model.

Abstract: A method of automatically capitalizing text utilizes a capitalization model. The capitalization model is trained from data that is taken from documents associated with a particular user. In particular, documents that are authored by the user such as e-mails, are used to train the model.

Abstract: The invention concerns a calibration device for the pressure of a scraping device blade, which abuts the periphery of a roller or cylinder, in particular, of a paper machine, a printing machine, a calender or the like, comprising a holding blade, a sensor holder mounted thereto, and a pressure sensor, wherein the holding blade, the sensor holder and the pressure sensor are positioned such that the position of the pressure sensor on the periphery of the roller or cylinder corresponds to the position of abutment of the blade. The invention also concerns a method for adjusting a contact pressure of a blade which abuts the periphery of a roller or cylinder, in particular of a paper machine, a printing machine, a calender or the like.

Abstract: An automatic speech recognition system recognizes user changes to dictated text and infers whether such changes result from the user changing his/her mind, or whether such changes are a result of a recognition error. If a recognition error is detected, the system uses the type of user correction to modify itself to reduce the chance that such recognition error will occur again. Accordingly, the system and methods provide for significant speech recognition learning with little or no additional user interaction.