Dragon Systems, Inc.

International Business Machines Corporation

Frank J McGrath

Joel M Gould

Joel W Parke

Jed M Roberts

Steven D Squires

Peter S Heitman

William C Tobin

Elizabeth E Steele

Mark Metheny

Dean G Sturtevant

James K Baker

Robert Bruce Levy

Kenneth Sandler

Vittorio R Castelli

Lawrence David Bergman

Steven Wesley Walton

Mark Frederick Victory

Serge E Lucio

In accordance with one embodiment of the invention, there is provided a method for modularizing testing of a software product. The method comprises receiving user input indicating a source and a destination of test content; and linking modification of the test content at the destination to modification of the test content at the source. In further related embodiments, receiving the user input may comprise receiving input of a copy/paste gesture for the test content, or a drag/drop gesture for the test content. The linking may comprise using a globally unique identifier to identify each test step of a test document; and using the globally unique identifier as a pointer from the destination to the source of the test content. The method may also comprise rendering the test content to the user as if the test content was in-line at the destination.

System and method for software product test modularization

An apparatus and method for maintaining a collection of test scripts. At least one test script module is identified that is common to at least two unmodularized test scripts that are each associated with a common application. The at least two unmodularized test scripts are divided into modularized test scripts that each comprise the at least one test script module. An update to the common application is received and at least one of the at least one test script modules is updated in response to the update of the common application. The updating of each of the at least one of the at least one test script module causes a corresponding update to the at least two unmodularized test scripts.

Semi-automated update of application test scripts

A method, system and apparatus for locating a testable object in a functional testing tool is provided. The method can include comparing object properties for a mapped testable object to object properties for each of a set of candidate testable objects organized in a hierarchy, computing an anchor object in the hierarchy, and determining a best matching candidate testable object for the mapped testable object without requiring an exact match of the object properties while constraining the comparing and determining steps with respect to the anchor object.

Locating a testable object in a functional testing tool

A method, system and apparatus for locating a testable object in a functional testing tool is provided. The system can include a record process configured to produce both a test script and an object mapping, and a playback process configured to process the test script. An object manager can be coupled to the playback process and configured to locate among candidate objects in a GUI, a candidate object having a set of properties which partially matches another set of properties associated with a corresponding test object in the object mapping.

Fuzzy location of a testable object in a functional testing tool

The present invention is a system, method and apparatus for disambiguating two like objects in two separate application containers. In accordance with the present invention, a pointer to one of the application containers can be obtained. Subsequently, the pointer can be provided as a parameter to a function call for an object in the one of the separate application containers. Notably, the application container can include one of a content browser and a distributable application.

Disambiguating like testable objects in a functional testing tool

A word recognition system can: respond to the input of a character string from a user by limiting the words it will recognize to words having a related, but not necessarily the same, string; score signals generated after a user has been prompted to generate a given word against words other than the prompted word to determine if the signal should be used to train the prompted word; vary the number of signals a user is prompted to generate to train a given word as a function of how well the training signals score against each other or prior models for the prompted word; create a new acoustic model of a phrase by concatenating prior acoustic models of the words in the phrase; obtain information from another program running on the same computer, such as its commands or the context of text being entered into it, and use that information to vary which words it can recognize; determine which program unit, such as an application program or dialog box, currently has input focus on its computer and create a vocabulary state associated with that program unit into which vocabulary words which will be made active when that program group has the focus can be put; detect the available computational resources and alter the instructions it executes in response; test if its ability to respond to voice input has been shut off without user confirmation, and, if so, turn that ability back on and prompt the user to confirm if that ability is to be turned off, store both a first and a second set of models for individual vocabulary words and enable a user to selectively cause the recognizer to disregard the second set of models for a selected word; and/or score a signal representing a given word against models for that word from different word model sets to select which model should be used for future recognition.

Apparatuses and methods for training and operating speech recognition

A word recognition system can: respond to the input of a character string from a user by limiting the words it will recognize to words having a related, but not necessarily the same, string, score signals generated after a user has been prompted to generate a given word against words other than the prompted word to determine if the signal should be used to train the prompted word; vary the number of signals a user is prompted to generate to train a given word as a function of how well the training signals score against each other or prior models for the prompted word; create a new acoustic model of a phrase by concatenating prior acoustic models of the words in the phrase; obtain information from another program running on the same computer, such as its commands or the context of text being entered into it, and use that information to vary which words it can recognize; determine which program unit, such as an application program or dialog box, currently has input focus on its computer and create a vocabulary state associated with that program unit into which vocabulary words which will be made active when that program group has the focus can be put; detect the available computational resources and alter the instructions it executes in response; test if its ability to respond to voice input has been shut off without user confirmation, and, if so, turn that ability back on and prompt the user to confirm if that ability is to be turned off; store both a first and a second set of models for individual vocabulary words and enable a user to selectively cause the recognizer to disregard the second set of models for a selected word; and/or score a signal representing a given word against models for that word from different word model sets to select which model should be used for future recognition.

Apparatuses and method for training and operating speech recognition

A word recognition system is disclosed for converting spoken utterances into either text or commands. The system runs on a platform capable of running a plurality applications. Text and commands are sent from a word recognition application to one or more user applications. In addition, information pertaining to the state of the user applications is sent back to the word recognition application. Word recognition probabilities are modified based the information received from the user applications. As a result, the probabilities of recognizing a spoken utterance as a particular command will be greater when that command is active in the user application then when it is not active. Also, text strings will be assigned higher probabilities when they are appropriate for the present state of the user application.

Method of speech command recognition with dynamic assignment of

A computerized system performs word recognition. It does pattern matching to select which vocabulary words appears, according to the pattern matching, to most probably correspond to word signals it seeks to recognize. It produces an output corresponding to the vocabulary words selected at a movable cursor position in a body of text. The system improves its recognition by obtaining information about the linguistic context of the current cursor position in the body of text and using that information to vary the probability of which one or more words is selected by its pattern matching. The pattern matching can be speech recognition performed on signals representing the sound of spoken words. In some embodiments, the word recognition is executed on a computer system capable of running multiple programs at once; the recognized word is output to another program for insertion at a cursor position in that other program; and the linguistic context information is obtained from data structures created by the other program.

Word recognition system using language context at current cursor

A computerized word recognition system, such as a speech recognition system, stores word models of a first and second set for each of a plurality of vocabulary words. The system has a user interface which enable a user to selectively prevent the use of a second set's word model for a selected word. Often the first set of word models are spelled word models, such as models represented by a sequence of phonetic component models, each of is derived from similar speech sounds occurring in different words. In such systems the second set of word models are custom words models derived largely from word signals which are presumed to correspond only to the model's associated word. In many embodiments, the user interface allows a user to select to stop using a selected word's custom, or second set, model by selecting a menu or control window of a user interface. It is preferred that the system automatically create custom models when word signals presumed to correspond to a given word score poorly against the word's spelled model. It is also preferred that the system respond to a command to delete a word's custom model by increasing, in subsequent adaptive training of word components for the word's spelled model, the weight given to information from such subsequent word signals relative to model information previously associated with such word components. This is done, in such a case, to compensate for the fact that past training data for the word has probably been corrupted.

Word recognition system which stores two models for some words and

A word recognition system detects the computational resources available to it, such as the speed or number of processors, or whether there is DSP hardware, and alters the instructions it executes in response. The system can be a word recognition program designed to run on different computers having different computational resources. The program receives user generated word signals representing words to be recognized; performs pattern matching on them to select which vocabulary words most probably correspond to such word signals; detects if certain computational resources are available; and varies the instructions it executes in response. In many embodiments the system is a speech recognition program. The word recognition program can vary the computational intensity of its signal processing as a function of available computational resources. Preferably it can match the same word models against representations of word signals produced both by its more and less intensive signal processing. For example, the more intense signal processing might take more frequent FFTs, but average them over the same time period at which the less intense signal processing takes FFTs, so both can represent word signals with the same data structure. If the program detects both a CPU and a DSP processor, it can cause the DSP to determine when the program should interrupt the CPU. The program can also vary the rate at which it filters relatively low scoring words out of consideration during the recognition process as a function of the level of available computational resources.

Word recognition system which alters code executed as a function of

A computerized speech recognition system creates acoustic models of phrases by concatenating acoustic models for individual words. The system stores an acoustic word model and spelling for each of its vocabulary words. When it receives the spelling of a multi-word phrase to be treated as a new vocabulary word, it stores that multi-word spelling as the spelling of the new vocabulary word, and a new acoustic model created by concatenating the acoustic word models of previous vocabulary words whose spellings correspond to words in the multi-word spelling as the acoustic model for the new word. The system can then perform speech recognition by comparing acoustic signals against the word models of stored vocabulary words, including those representing such multi-word phrases. Preferably when a multi-word model is formed, the individual acoustic models concatenated are modified to represent the coarticulation which takes place between words spoken continuously. This can be done by representing word models as sequences of individual phonemes, individual phonemes by phoneme-in-context models, and coarticulation by modifying the phoneme-in-context models of phonemes adjacent the boundary between concatenated words models to reflect the context of the phonemes on the other side of such word boundaries. The system can be a discrete word recognizer. The multi-word phrases can be selected by a user or be obtained from other programs running on the same computer as the speech recognizer, such as from one or more commands available in another program.

Speech recognition system which creates acoustic models by concatenating

A word recognition system can: respond to the input of a character string from a user by limiting the words it will recognize to words having a related, but not necessarily the same, string; score signals generated after a user has been prompted to generate a given word against words other than the prompted word to determine if the signal should be used to train the prompted word; vary the number of signals a user is prompted to generate to train a given word as a function of how well the training signals score against each other or prior models for the prompted word; create a new acoustic model of a phrase by concatenating prior acoustic models of the words in the phrase; obtain information from another program running on the same computer, such as its commands or the context of text being entered into it, and use that information to vary which words it can recognize; determine which program unit, such as an application program or dialog box, currently has input focus on its computer and create a vocabulary state associated with that program unit into which vocabulary words which will be made active when that program group has the focus can be put; detect the available computational resources and alter the instructions it executes in response; test if its ability to respond to voice input has been shut off without user confirmation, and, if so, turn that ability back on and prompt the user to confirm if that ability is to be turned off; store both a first and a second set of models for individual vocabulary words and enable a user to selectively cause the recognizer to disregard the second set of models for a selected word; and/or score a signal representing a given word against models for that word from different word model sets to select which model should be used for future recognition.

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Wellesley, MA, United States of America

Frank J McGrath