Abstract: A modular ACD system is configured to automate clinical documentation and includes a machine vision system configured to obtain machine vision encounter information concerning a patient encounter. An audio recording system is configured to obtain audio encounter information concerning the patient encounter. A compute system is configured to receive the machine vision encounter information and the audio encounter information.

Abstract: A method, computer program product, and computing system for receiving content from a third-party; processing the content to predict the disclosure of sensitive information; and obscuring the sensitive information from a platform user.

Abstract: A system for and method of characterizing a target application acoustic domain analyzes one or more speech data samples from the target application acoustic domain to determine one or more target acoustic characteristics, including a CODEC type and bit-rate associated with the speech data samples. The determined target acoustic characteristics may also include other aspects of the target speech data samples such as sampling frequency, active bandwidth, noise level, reverberation level, clipping level, and speaking rate. The determined target acoustic characteristics are stored in a memory as a target acoustic data profile. The data profile may be used to select and/or modify one or more out of domain speech samples based on the one or more target acoustic characteristics.

Abstract: A system for and method of characterizing a target application acoustic domain analyzes one or more speech data samples from the target application acoustic domain to determine one or more target acoustic characteristics, including a CODEC type and bit-rate associated with the speech data samples. The determined target acoustic characteristics may also include other aspects of the target speech data samples such as sampling frequency, active bandwidth, noise level, reverberation level, clipping level, and speaking rate. The determined target acoustic characteristics are stored in a memory as a target acoustic data profile. The data profile may be used to select and/or modify one or more out of domain speech samples based on the one or more target acoustic characteristics.

Abstract: A graphical user interface is described for human guided audio source separation in a multi-speaker automated transcription system receiving audio signals representing speakers participating together in a speech session. A speaker avatar for each speaker is distributed about a user interface display to suggest speaker positions relative to each other during the speech session. There also is a speaker highlight element on the interface display for visually highlighting a specific speaker avatar corresponding to an active speaker in the speech session to aid a human transcriptionist listening to the speech session to identify the active speaker. A speech signal processor performs signal processing of the audio signals to isolate an audio signal corresponding to the highlighted speaker avatar.

Abstract: Training speech recognizers, e.g., their language or acoustic models, using actual user data is useful, but retaining personally identifiable information may be restricted in certain environments due to regulations. Accordingly, a method or system is provided for enabling training of a language model which includes producing segments of text in a text corpus and counts corresponding to the segments of text, the text corpus being in a depersonalized state. The method further includes enabling a system to train a language model using the segments of text in the depersonalized state and the counts. Because the data is depersonalized, actual data may be used, enabling speech recognizers to keep up-to-date with user trends in speech and usage, among other benefits.

Abstract: Training speech recognizers, e.g., their language or acoustic models, using actual user data is useful, but retaining personally identifiable information may be restricted in certain environments due to regulations. Accordingly, a method or system is provided for enabling training of an acoustic model which includes dynamically shredding a speech corpus to produce text segments and depersonalized audio features corresponding to the text segments. The method further includes enabling a system to train an acoustic model using the text segments and the depersonalized audio features. Because the data is depersonalized, actual data may be used, enabling speech recognizers to keep up-to-date with user trends in speech and usage, among other benefits.

Abstract: Typical textual prediction of voice data employs a predefined implementation arrangement of a single or multiple prediction sources. Using a predefined implementation arrangement of the prediction sources may not provide a good prediction performance in a consistent manner with variations in voice data quality. Prediction performance may be improved by employing adaptive textual prediction. According to at least one embodiment determining a configuration of a plurality of prediction sources, used for textual interpretation of the voice data, is determined based at least in part on one or more features associated with the voice data or one or more a-priori interpretations of the voice data. A textual output prediction of the voice data is then generated using the plurality of prediction sources according to the determined configuration. Employing an adaptive configuration of the text prediction sources facilitates providing more accurate text transcripts of the voice data.

Abstract: Training speech recognizers, e.g., their language or acoustic models, using actual user data is useful, but retaining personally identifiable information may be restricted in certain environments due to regulations. Accordingly, a method or system is provided for enabling training of a language model which includes producing segments of text in a text corpus and counts corresponding to the segments of text, the text corpus being in a depersonalized state. The method further includes enabling a system to train a language model using the segments of text in the depersonalized state and the counts. Because the data is depersonalized, actual data may be used, enabling speech recognizers to keep up-to-date with user trends in speech and usage, among other benefits.

Abstract: Training speech recognizers, e.g., their language or acoustic models, using actual user data is useful, but retaining personally identifiable information may be restricted in certain environments due to regulations. Accordingly, a method or system is provided for enabling training of an acoustic model which includes dynamically shredding a speech corpus to produce text segments and depersonalized audio features corresponding to the text segments. The method further includes enabling a system to train an acoustic model using the text segments and the depersonalized audio features. Because the data is depersonalized, actual data may be used, enabling speech recognizers to keep up-to-date with user trends in speech and usage, among other benefits.

Abstract: Typical textual prediction of voice data employs a predefined implementation arrangement of a single or multiple prediction sources. Using a predefined implementation arrangement of the prediction sources may not provide a good prediction performance in a consistent manner with variations in voice data quality. Prediction performance may be improved by employing adaptive textual prediction. According to at least one embodiment determining a configuration of a plurality of prediction sources, used for textual interpretation of the voice data, is determined based at least in part on one or more features associated with the voice data or one or more a-priori interpretations of the voice data. A textual output prediction of the voice data is then generated using the plurality of prediction sources according to the determined configuration. Employing an adaptive configuration of the text prediction sources facilitates providing more accurate text transcripts of the voice data.

Abstract: A graphical user interface is described for human guided audio source separation in a multi-speaker automated transcription system receiving audio signals representing speakers participating together in a speech session. A speaker avatar for each speaker is distributed about a user interface display to suggest speaker positions relative to each other during the speech session. There also is a speaker highlight element on the interface display for visually highlighting a specific speaker avatar corresponding to an active speaker in the speech session to aid a human transcriptionist listening to the speech session to identify the active speaker. A speech signal processor performs signal processing of the audio signals to isolate an audio signal corresponding to the highlighted speaker avatar.

Abstract: A control apparatus for enabling a user to communicate by speech with a processor-controlled apparatus, 1) controls a display of text data which includes a speech link that can be activated by a spoken command, 2) determines the location of a cursor displayed on a display from gaze input information, 3) changes a shape of the cursor when the cursor is located over the speech link, and 4) outputs a prompt identifying speech commands that can be used to activate the speech link when the cursor is displayed on the display in a changed state for a predetermined time located over the speech link.

Abstract: Initially an embedding module (22) determines an embedding of a lattice in a two-dimensional plane. The embedding module (22) then processes the initial embedding to generate a planar graph in which no links cross. The planar graph is then simplified by a link encoding module (24) and data representing the lattice structure is generated by a shape encoding module (26)—in which the simplified planar graph is represented by a shape encoding (42) identifying the numbers of links bounding areas defined by the planar graph and data identifying the locations of those areas within the planar graph; and a link list (43) identifying the modifications made to the lattice structure by the link encoding module (24). These encodings are such that the same substructures within a lattice are represented using the same data and hence are suitable for compression using conventional techniques.

Abstract: A control apparatus is provided for enabling a user to communicate by speech with processor-controlled apparatus (1) having a display (44) for displaying screens to a user, an audio input (42) for receiving speech data from the user and a pointing device (40) for providing data for enabling the screen location at which the user's attention is directed to be determined.

Abstract: A Control apparatus for controlling communication between a user and at least one processor controlled device, such as a printer or copier, capable of carrying out at least one task. The control includes a processor configured to conduct a dialog with the user to determine the task that the user wishes the device to carry out; instruct the device to carry out the determined task; receive event information related to events; determine whether the user is involved with another task when the event information is received; identify interrupt status information associated with at least one of the event for which event information is received and said other task; determine whether or not the user can be interrupted on the basis of the identified interrupt status information; and advise the user of received event information.

Abstract: A control apparatus controls the display of text data which includes a speech link that can be activated by spoken command. The shape of a pointing device cursor displayed on a display is then changed by the apparatus when the pointing device cursor is located over the speech link included in displayed text data. The apparatus is arranged to output a prompt identifying speech commands that can be used to activate the speech link if the pointing device cursor is displayed on a display located over the speech link in a changed state for a predetermined time.

Abstract: A machine interface allows a user to select a machine operation. A plurality of questions are stored for output to a user. A score indicating the likelihood that the user will select a machine operation is stored for each corresponding machine operation. A next question for output to the user is selected from the stored questions by determining, for each of a plurality of the questions, an average of the least number of questions required to be answered by the user to arrive at each machine operation weighted by the respective scores, and selecting the question having the lowest average number. The selected question is output and an answer is received from the user. In response to the input answer a machine operation is carried out and/or the stored scores for each of the plurality of machine operations is adjusted. At least one further selection of a next question is then carried out for output to the user using the adjusted stored scores.

Abstract: Control apparatus for controlling communication between a user and at least one processor controlled device, such as a printer or copier, capable of carrying out at least one task is described.

Abstract: A control apparatus (2) has a user interface manager (21;22) having at least one interface module (215,214,213,216,211;221,222,223,224) adapted to receive data for a corresponding user interface mode. A dialogue manager (201) associated with a dialogue interpreter (202) is arrange to conduct a dialogue with the user in accordance with mark-up language document files supplied to the dialogue conductor. In an embodiment, the control apparatus determines any user interface mode or modes specified by a received mark-up language document, determines whether the user interface manager has an interface module for the specified user interface mode or modes and, if not, obtains an interface module for that interface mode. In another embodiment, the mark-up language document files supplied to the user interface manager specify a type and/or accuracy or confidence level for the interface mode and the control apparatus selects the interface module or modules to be used on the basis of this information.