Abstract: A digital display of an active window is provided. An application, separate from the application providing the digital display, is capable of identifying an artifact in the active window of the digital display without a priori knowledge or an application programming interface to the application providing the digital display. The artifacts may be alpha-numeric artifacts or non-alpha-numeric artifacts. The active window may be examined in multiple dimensions including 2 dimensions, 3 dimensions or more. Alpha-numeric artifacts, once identified, are pre-processed to remove anti-aliasing to allow for processor optical recognition.

Abstract: A method to allow for real-time dictation and transcription with multiple remote endpoints is provided. The method comprises evoking a primary application and a client device APP (or APP) to work with a remote hosted application to process audio for the primary application. The APP connects to the hosted application, and the hosted application receives and processes the audio. The hosted application returns the text to the client device, which text populates the primary application. The APP and/or the hosted application also transmits the text to a remote endpoint, such as, for example, a desktop computer or a laptop computer where the user can interact with the primary application and the text returned by the hosted application.

Abstract: The technology of the present application provides software as a service (SaaS) executing on a server in a cloud or network. The SaaS receives data from a mobile device of a user over the network. The SaaS processes the data and returns the processed data to a client application executing on a client device of the user, which user is the same as the user of the mobile device wherein there is no direct communication link, wireless or wired, between the mobile device and the client device. In one aspect, the technology of the present application provides the mobile device as a smartphone and a microphone application to be executed on the smartphone.

Abstract: The technology of the present application provides a method and apparatus to manage speech resources. The method includes using a text recognizer to detect a change in a speech application that requires the use of different resources. On detection of the change, the method loads the different resources without the user needing to exit the currently executing speech application.

Abstract: The technology of the present application provides software as a service (SaaS) executing on a server in a cloud or network. The SaaS receives data from a mobile device of a user over the network. The SaaS processes the data and returns the processed data to a client application executing on a client device of the user, which user is the same as the user of the mobile device wherein there is no direct communication link, wireless or wired, between the mobile device and the client device. In one aspect, the technology of the present application provides the mobile device as a smartphone and a microphone application to be executed on the smartphone.

Abstract: To attain the advantages and in accordance with the purpose of the technology of the present application, apparatuses, systems, and methods to evaluate a user audio profile are provided. The evaluation of a user audio profile allows for identification of potential causes of poor performing user audio profiles and potential types of remediation to increase the performance of the user audio profile.

Abstract: The technology of the present application provides a speech recognition system with at least two different speech recognition engines or a single engine speech recognition engine with at least two different modes of operation. The first speech recognition being used to match audio to text, which text may be words or phrases. The matched audio and text is used by a training module to train a user profile for a natural language speech recognition engine, which is at least one of the two different speech recognition engines or modes. An evaluation module evaluates when the user profile is sufficiently trained to convert the speech recognition engine from the first speech recognition engine or mode to the natural language speech recognition or mode.

Abstract: The technology of the present application provides a modified language model to allow for the recognition of speech containing types of disfluency. The modified language model includes a plurality of n-grams where an n-gram comprises a sequent of words. The language model also has at least one synthetic n-gram where the synthetic is a naturally occurring n-gram combined with a disfluency token. The disfluency token is representative of multiple types of disfluency and multiple pronunciations thereof.

Abstract: A speech recognition engine is provided. The speech recognition engine combines multiple, different statistical language models to allow a single instance of a speech recognition engine to recognize words applicable to a first group of users and a second group of users where the second group of users is a subset of the first group of users. At least one of the multiple, different statistical language models comprises words specific to the second group of users.

Abstract: The technology of the present application provides a method and apparatus to manage speech resources. The method includes using a text recognizer to detect a change in a speech application that requires the use of different resources. On detection of the change, the method loads the different resources without the user needing to exit the currently executing speech application.

Abstract: The technology of the present application provides a speech recognition system with at least two different speech recognition engines or a single engine speech recognition engine with at least two different modes of operation. The first speech recognition being used to match audio to text, which text may be words or phrases. The matched audio and text is used by a training module to train a user profile for a natural language speech recognition engine, which is at least one of the two different speech recognition engines or modes. An evaluation module evaluates when the user profile is sufficiently trained to convert the speech recognition engine from the first speech recognition engine or mode to the natural language speech recognition or mode.

Abstract: The technology of the present application provides a method and apparatus to managing resources for a system using voice recognition. The method and apparatus includes maintaining a database of historical data regarding a plurality of users. The historical database maintains data regarding the training resources required for users to achieve an accuracy score using voice recognition. A resource calculation module determines from the historical data an expected amount of training resources necessary to train a new user to the accuracy score.

Abstract: The technology of the present application provides a method and apparatus to manage speech resources. The method includes detecting a change in a speech application that requires the use of different resources. On detection of the change, the method loads the different resources without the user needing to exit the currently executing speech application. The apparatus provides a switch (which could be a physical or virtual switch) that causes a speech recognition system to identify audio as either commands or text.

Abstract: The technology of the present application provides a method and apparatus to allow for dynamically updating a language model across a large number of similarly situated users. The system identifies individual changes to user profiles and evaluates the change for a broader application, such as, a dialect correction for a speech recognition engine, as administrator for the system identifies similarly situated user profiles and downloads the profile change to effect a dynamic change to the language model of similarly situated users.

Abstract: An automated system to build a user audio profile for a natural or continuous language speech to text dictation/transcription system is provided. The system uses previously recorded audio files that may have been already transcribed. The previously recorded audio file is split into a plurality of smaller audio files of about 15 seconds in length. The plurality of smaller audio files are matched to the transcribed text (e.g., small text files) or the smaller audio files are transcribed. All, some, or a selection of the small audio files and the small text files are linked as a training pair. The training pair may be edited in certain embodiments herein, both the text and the audio. The training pairs are submitted to the server to build the initial user audio profile.

Abstract: The technology of the present application provides apparatuses and methods that may be used to generate the smallest language model for a continuous speech recognition engine that covers a given speaker's speech patterns. The apparatuses and methods start with a generic language model that is an approximation to the given speaker's speech patterns. The given speaker generates corrected transcripts that allows for the generation of a user specific language model. Once the user specific language model is sufficiently robust, the continuous speech recognition system may replace the generic language model with the user specific language model.

Abstract: A distributed dictation/transcription system is provided. The system provides a dictation manager having a data port to receive and transmit audio signals. The dictation manager includes a dictation server selector to a dictation server to transcribe the audio based on whether the dictation server already has a user profile uploaded.

Abstract: A distributed dictation/transcription system is provided. The system provides a client station, dictation manager, and dictation server connected such that the dictation manager can select a dictation server to transcribe audio from the client station. The dictation manager has a data port to at least receive and transmit audio, and receive and transmit data or text. A job queue at the dictation manager holds the audio to be provided to the dictation servers. When the dictation manager identifies an available dictation server, the dictation manager reviews all jobs in the queue. If a job in the queue has a user profile that has been previously uploaded to the available dictation server, the dictation manager transmits the audio to the dictation server.

Abstract: A computer-implemented method for simplifying the pasting of textual transcriptions from a transcription engine into an application is described. An audio file is sent to a transcription engine. A textual transcription file of the audio file is received from the transcription engine. The textual transcription file is automatically loaded into a copy buffer. The textual transcription file is pasted from the copy buffer into an application.

Abstract: The present invention provides a data device to speech service bridge. The bridge allows packet-based devices, such as, for example, the PDA and 2.5/3G cellular phone, to access voice services.