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: A method, apparatus, or system for verifying the delivery of home health care is provided. The method, apparatus, or system includes obtaining information relating to at least one of a provider name, a location of treatment, a date of treatment, and a patient name from a billing application. Similar information is obtained from an electronic health record and/or data appended or associated with the electronic health record. A comparison between the billing application and electronic health record data provides data to verify the delivery of the home health care by the identified health care provider to the patient.

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 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 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: 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: 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 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: A dictation system that allows using trainable code phrases is provided. The dictation system operates by receiving audio and recognizing the audio as text. The text/audio may contain code phrases that are identified by a comparator that matches the text/audio and replaces the code phrase with a standard clause that is associated with the code phrase. The database or memory containing the code phrases is loaded with matched standard clauses that may be identified to provide a hierarchal system such that certain code phrases may have multiple meanings depending on the user.