International Business Machines Corporation

Amado Nassiff

Peter J Guasti

Frank Fado

Harvey M Ruback

Ronald E VanBuskirk

Huifang Wang

Ronald Van Buskirk

Steven Gary Woodward

Ronald E Vanbuskirk

Gary Robert Hanson

Kerry Alan Ortega

Daniel E Badt

Edwin A Rodriguez

Carl A Smith

James Robert Lewis

Raimo Bakis

Francis Fado

Joseph Celi, Jr

Barbara E Ballard

William Carleton Wimer, Ii

Robert Selby Sielken

Frank J Castaneda

Russell S Padgett

David Lee Griffin

Christopher James Martinez

Karen Neidlinger Momenee

Spencer G Rauenzahn, Ii

A method for Internet protocol (IP) address discovery in a rapidly changing network environment is presented. A server and a client use an adaptive discovery polling process to determine an optimum heartbeat interval that minimizes network traffic and allows the server to monitor the client's source address. The client and server exchange discovery poll messages and heartbeat messages at varying time intervals in order to identify a computer network's timeout period or a network address translator's message block timeout period. Once the timeout period is identified, the client sends heartbeat messages to the server at an 'optimum heartbeat interval' that is less than the identified timeout period in order to maintain the network connection. As a result, the server is able to send messages to the client without delay.

Method for IP address discovery in rapidly changing network environment

A method for navigating in a vehicle includes the steps of identifying navigation information for at least one destination. The navigation information is stored in at least a first memory remote from the vehicle. The navigation information is transferred from the first memory to a navigation device in the vehicle. The vehicle can then be navigated to the destination using the navigation device and the navigation information.

Vehicle navigation method

The method of identifying excess noise in a computer system includes first recording a silence sample; second recording an isolated noise sample while operating a computer system component in isolation from other computer system components; comparing signal characteristics of the silence sample with signal characteristics of the isolated noise sample; and, attributing the isolated noise sample to the isolated computer component when the signal characteristics of the silence sample differ by a preset threshold from the signal characteristics of the isolated noise sample. The inventive method can further include logging the signal characteristics of the silence sample and the isolated noise sample; reporting excess noise identified in the identifying step; and, suggesting a remedy for the identified excess noise.

Process for identifying excess noise in a computer system

A method and a system for improving recall of speech data in a computer speech system can include a plurality of speech cache management steps including providing a speech cache, receiving a speech system input and identifying a speech event in the received speech system input, the speech event comprising speech data. Subsequently, the speech data can be compared to pre-determined speech cache entry criteria; and, if the speech data meets one of the pre-determined entry criteria, at least one entry can be added to the speech cache, the at least one entry corresponding to the speech data. Additionally, the speech data can be compared to pre-determined speech cache exit criteria; and, if the speech data meets one of the pre-determined exit criteria, at least one entry can be purged from the speech cache, the at least one entry corresponding to the speech data. The entry criteria can include frequently used speech data, recently used speech data, and important speech data. Similarly, the exit criteria can include least frequently used speech data associated with each entry in the speech cache, least recently used speech data associated with each entry in the speech cache and least important speed data associated with each entry in the speech cache.

Method of managing a speech cache

A computer system has a notification manager for playing a message to a user by selecting one of a plurality of audio notifications. The method includes the step of setting a priority level for each notification arriving into a queue. The notification is inserted into a position in the queue based upon the priority level of the notification, such that the audio notifications at the queue top have a generally higher priority than audio notifications at the queue bottom. The notification at the top of the queue can be selected if the priority level of the notification is greater than a predetermined gate level. Once a notification is selected, a message corresponding to the selected notification is played to the user.

Audio notification management system

A method for enabling a user to proactively reduce the likelihood of audio feedback in an application requiring audio input and output, comprising the steps of: generating a graphical user interface (GUI) display screen including a first area for displaying information about preventing audio feedback and a second area for user selections and controls; displaying a list of available audio outputs in the second area; prompting the user to select one of the audio outputs from the list; prompting the user to select one of a plurality of muting options for each selected one of the audio outputs; and, displaying in the GUI display screen an explanation for each one of the plurality of muting options, whereby muting selections for proactively reducing the likelihood of audio feedback can be made based on user experience and knowledge. Only one of the muting option explanations is displayed at a time, responsive to the user selection of one of the muting options.

Preventing audio feedback

A method for adjusting audio input signal gain in a speech system can include seven steps. First, an upper and a lower threshold can be predetermined in which the upper and lower threshold define an optimal range of audio data signal amplitude measurements. Second, a frame of unpredicted digital audio data samples can be received. Each sample can indicate an amplitude measurement of the audio data signal at a particular point in time. Third, a maximum signal amplitude can be calculated for a configurable measurement percentile of the unpredicted digital audio data samples. Fourth, the audio input signal gain can be incrementally adjusted downward if the maximum signal amplitude exceeds the upper threshold. Conversely, fifth, the audio input signal gain can be incrementally adjusted upward if the maximum signal amplitude falls below the lower threshold. Sixth, additional frames of unpredicted digital audio data samples can be received. Finally, seventh, each of the third through the sixth steps can be repeated with the received additional frames until the calculated maximum signal amplitude falls within the optimal range of audio signal amplitude.

Method for dynamic adjustment of audio input gain in a speech system

A method for guiding a user through trouble shooting a wrong audio source among a plurality of audio sources, comprises the steps of: (a) generating a GUI display screen including a first area for displaying information about testing an audio input device, a second area for displaying instructions and status information and for providing dynamic feedback and a third area for user selections and controls; (b) displaying a list of available audio sources in said third area; (c) prompting said user to select an audio source from said list; (d) prompting said user to test said selected audio source; (e) in the event said test is unsuccessful, prompting said user to select any other audio source identified in said third area; (f) prompting said user to test said any other audio source in said list; and, (g) repeating steps (e) and (f) until one of said audio sources is tested successfully or each of said audio sources is tested unsuccessfully.

Trouble shooting a wrong audio source

A method for connecting and optimizing audio input devices, comprises the steps of: determining an audio input type; generating a first GUI display screen for prompting and enabling user selection of an audio input device; generating a second GUI display screen for prompting and enabling user connection of the audio input device; testing the connected audio input device; configuring audio settings of the connected audio input device; and, storing for later retrieval an association of the connected audio input device and the configured audio settings. The audio settings are configured and the association is stored only if the testing step is successful. The second GUI display screen can include a device specific image and device specific instructions.

Connecting and optimizing audio input devices

In a computer system adapted for text-to-speech playback, a method for instructing a user in performing a task having a plurality of steps can include retrieving a textual instruction from a location in an electronic storage device of the computer system. The textual instruction can correspond to one or more of the steps in the task. The textual instruction can be displayed in a task automation user interface, and a text-to-speech (TTS) conversion of the textual instruction can be executed. The steps can be repeated until all textual instructions corresponding to each step in the task have been retrieved and TTS converted.

Task automation user interface with text-to-speech output

In a speech recognition system, a method and system for updating a language model during a correction session can include automatically comparing dictated text to replacement text, determining if the replacement text is on an alternative word list if the comparison is close enough to indicate that the replacement text represents correction of a mis-recognition error rather than an edit, and updating the language model without user interaction if the replacement text is on the alternative word list. If the replacement text is not on the alternative word list, a comparison is made between dictated word digital information and replacement word digital information, and the language model is updated if the digital comparison is close enough to indicate that the replacement text represents correction of a mis-recognition error rather than an edit.

Smart correction of dictated speech

A method and system for improving the speech command recognition accuracy of a computer speech recognition system uses event-based constraints to recognize a spoken command. The constraints are system states and events, which include system activities, active applications, prior commands and an event queue. The method and system is performed by monitoring events and states of the computer system and receiving a processed command corresponding to the spoken command. The processed command is statistically analyzed in light of the system events and states as well as according to an acoustic model. The system then identifies a recognized command corresponding to the spoken command.

Method and apparatus for improving speech command recognition accuracy using event-based constraints

A method for enabling user selectable input devices for dictation or transcription in a speech application, comprising the steps of: establishing a registry of dictation and transcription device descriptions, each of the descriptions including a device specific image, a device specific set of device-connecting instructions and a device specific list of audio configuration parameters; building dynamic tables containing information retrieved from the registry; establishing and storing a plurality of enrollments, each of the enrollments representing a speech file of user specific training data corresponding to at least one of a specific audio input device and a specific audio environment; and, generating GUI display screen using the information in at least one of the dynamic tables to enable user selection any input device in the registry for which one of the enrollments is available, for use as a dictation or transcription input to the speech application.

User selectable input devices for speech applications

A method and system efficiently identifies voice commands for a user of a speech recognition system. The method involves a series of steps including: receiving input from a user; monitoring the computer system to log system events and ascertain a current system state; predicting a probable next event according to the current system state and logged events; and identifying acceptable voice commands to perform the next event. The system events include commands, system control activities, timed activities, and application activation. These events are statistically analyzed in light of the current system state to determine the probable next event. The voice commands for performing the probable next event are displayed to the user.

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Boynton Beach, FL, United States of America

Amado Nassiff