Nvoq Incorporated

Charles Corfield

Brian Marquette

Michael Clark

David Mondragon

Todd Espy

Stephen L Bulick

Christopher S Kramp

Mark E Meyer

Adam M Ornstein

Bret Butcher

Jarek Foltynski

Richard Beach

Rebecca Heins

Daniel Morley

Peter Fox

Derek C Smith

E Peter Fox

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.

Recognition of artifacts in computer displays

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.

Systems and methods for a wireless microphone to access remotely hosted applications

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.

Apparatus and methods for dynamically changing a speech resource based on recognized text

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.

Systems and methods evaluating user audio profiles for continuous speech recognition

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.

Apparatus and methods using a pattern matching speech recognition engine to train a natural language speech recognition engine

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.

Language model for speech recognition to account for types of disfluency

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.

System and methods for combining finite state transducer based speech recognizers

Apparatus and methods for dynamically changing a language model based on recognized text

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.

Apparatus and methods for managing resources for a system using voice recognition

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.

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.

Apparatus and methods to update a language model in a speech recognition system

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.

System and methods to create and determine when to use a minimal user specific language model

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.

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Boulder, CO, United States of America

Charles Corfield