Abstract: An in-vehicle information system includes a camera and a controller that accept gesture input. A controller receives frames of video data and generates trajectory data for a movement of a hand in the video data. The controller uses a first hidden Markov model (HMM) to decode a sequence of strokes from the trajectory data, removes a starting and ending stroke to form an edited stroke sequence, and re-normalizes the strokes in the edited stroke sequence. The controller uses a second HMM corresponding to a predetermined set of characters to identify a character corresponding to the re-normalized edited stroke sequence.

Abstract: A method for verifying that a person is registered to use a telemedical device includes identifying an unprompted trigger phrase in words spoken by a person and received by the telemedical device. The telemedical device prompts the person to state a name of a registered user and optionally prompts the person to state health tips for the person. The telemedical device verifies that the person is the registered user using utterance data generated from the unprompted trigger phrase, name of the registered user, and health tips.

Abstract: A method of interaction with an in-vehicle information system includes receiving first and second inputs from an operator with first and second input devices, respectively. The method further includes identifying a service request corresponding to the first input, and a parameter of the service request with a value that is included in the second input with a controller in the in-vehicle information system. The controller executes stored program instructions to perform the identified service request with reference to the identified parameter.

Abstract: In a confidence computing method and system, a processor may interpret speech signals as a text string or directly receive a text string as input, generate a syntactical parse tree representing the interpreted string and including a plurality of sub-trees which each represents a corresponding section of the interpreted text string, determine for each sub-tree whether the sub-tree is accurate, obtain replacement speech signals for each sub-tree determined to be inaccurate, and provide output based on corresponding text string sections of at least one sub-tree determined to be accurate.

Abstract: A method of providing information assistance services includes generating a plurality of service requests for a plurality of request elements that are generated from a single client request received by a processor. The service requests are sent to both software application service providers that are executed by the processor and remote service providers that are connected to the local processor through a data network. The processor receives a plurality of service responses from the service providers, generating at least one output message element corresponding to the service responses, and sending the output message data to at least one output device that is operatively connected to the processor to produce a response to the client request.

Abstract: A method for speech recognition includes providing a source of geographical information within a vehicle. The geographical information pertains to a current location of the vehicle, a planned travel route of the vehicle, a map displayed within the vehicle, and/or a gesture marked by a user on a map. Words spoken within the vehicle are recognized by use of a speech recognition module. The recognizing is dependent upon the geographical information.

Abstract: A method for verifying that a person is registered to use a telemedical device includes identifying an unprompted trigger phrase in words spoken by a person and received by the telemedical device. The telemedical device prompts the person to state a name of a registered user and optionally prompts the person to state health tips for the person. The telemedical device verifies that the person is the registered user using utterance data generated from the unprompted trigger phrase, name of the registered user, and health tips.

Abstract: Embodiments of a dialog system that utilizes grammar-based labeling scheme to generate labeled sentences for use in training statistical models. During the process of training data development, a grammar is constructed manually based on the application domain or adapted from a general grammar rule. An annotation schema is created accordingly based on the application requirements, such as syntactic and semantic information. Such information is then included in the grammar specification. After the labeled grammar is constructed, a generation algorithm is then used to generate sentences for training various statistical models.

Abstract: A method for speech recognition includes providing a source of geographical information within a vehicle. The geographical information pertains to a current location of the vehicle, a planned travel route of the vehicle, a map displayed within the vehicle, and/or a gesture marked by a user on a map. Words spoken within the vehicle are recognized by use of a speech recognition module. The recognizing is dependent upon the geographical information.

Abstract: In accordance with one embodiment, a method of generating language models for speech recognition includes identifying a plurality of utterances in training data corresponding to speech, generating a frequency count of each utterance in the plurality of utterances, generating a high-frequency plurality of utterances from the plurality of utterances having a frequency that exceeds a predetermined frequency threshold, generating a low-frequency plurality of utterances from the plurality of utterances having a frequency that is below the predetermined frequency threshold, generating a grammar-based language model using the high-frequency plurality of utterances as training data, and generating a statistical language model using the low-frequency plurality of utterances as training data.

Abstract: A method for speech recognition includes providing a source of geographical information within a vehicle. The geographical information pertains to a current location of the vehicle, a planned travel route of the vehicle, a map displayed within the vehicle, and/or a gesture marked by a user on a map. Words spoken within the vehicle are recognized by use of a speech recognition module. The recognizing is dependent upon the geographical information.

Abstract: Embodiments of a dialog system that utilizes contextual information to perform recognition of proper names are described. Unlike present name recognition methods on large name lists that generally focus strictly on the static aspect of the names, embodiments of the present system take into account of the temporal, recency and context effect when names are used, and formulates new questions to further constrain the search space or grammar for recognition of the past and current utterances.

Abstract: Systems and methods are described that automatically control modules of dialog systems. The systems and methods include a dialog module that receives and processes utterances from a speaker and outputs data used to generate synthetic speech outputs as responses to the utterances. A controller is coupled to the dialog module, and the controller detects an abnormal output of the dialog module when the dialog module is processing in an automatic mode. The controller comprises a mode control for an agent to control the dialog module by correcting the abnormal output and transferring a corrected output to a downstream dialog module that follows, in a processing path, the dialog module. The corrected output is used in further processing the utterances.

Abstract: Embodiments of a dialog system that utilizes contextual information to perform recognition of proper names are described. Unlike present name recognition methods on large name lists that generally focus strictly on the static aspect of the names, embodiments of the present system take into account of the temporal, recency and context effect when names are used, and formulates new questions to further constrain the search space or grammar for recognition of the past and current utterances.

Abstract: Embodiments of a progressive feature selection method that selects features in multiple rounds are described. In one embodiment, the progressive feature selection method splits the feature space into tractable sub-spaces such that a feature selection algorithm can be performed on each sub-space. In a merge-split operation, the subset of features that the feature selection algorithm selects from the different sub-spaces are merged into subsequent sets of features. Instead of re-generating the mapping table for each subsequent set from scratch, a new mapping table from the previous round's tables is created by collecting those entries that correspond to the selected features. The feature selection method is then performed again on each of the subsequent feature sets and new features are selected from each of these feature sets. This feature selection-merge-split process is repeated on successively smaller numbers of feature sets until a single final set of features is selected.

Abstract: Embodiments of a feature generation system and process for use in machine learning applications utilizing statistical modeling systems are described. In one embodiment, the feature generation process generates large feature spaces by combining features using logical, arithmetic and/or functional operations. A first set of features in an initial feature space are defined. Some or all of the first set of features are processed using one or more arithmetic, logic, user-defined combinatorial processes, or combinations thereof, to produce additional features. The additional features and at least some of the first set of features are combined to produce an expanded feature space. The expanded feature space is processed through a feature selection and optimization process to produce a model in a statistical modeling system.

Abstract: Systems and methods are described that automatically generate interactive systems configured for collecting dialog data of human-machine interactions in dialog systems. The systems and methods comprise receiving a task flow that describes operations of a dialog system. A formal description of the task flow is generated, and an interactive system comprising a graphical user interface (GUI) is automatically generated from the formal description. The GUI consists of templates for control of the dialog system and real-time collection and annotating of dialog data during a live dialog between only the dialog system and callers to the dialog system. The dialog data consists of data of the live dialog.

Abstract: Embodiments of a name recognition process for use in dialog systems are described. In one embodiment, the name recognition process assigns weighting values to names used in a dialog based on the usage of these names. This process takes advantage of the general tendency of people to speak names, either full or partial, only after they have heard or read these names. Name input is taken in several different forms, including a static background database that contains all possible names, a background database that contains commonly used names (such as common trademarks or references), a database that contains names from a user model, and a dynamic database that constantly takes the names just mentioned. The names are then appended with proper weighting values. A high weight is given to names that have been mentioned recently, a lower weight is given to common names, and a lowest weight is given to names for the ones that have never been used or mentioned.

Abstract: A method for speech recognition includes providing a source of geographical information within a vehicle. The geographical information pertains to a current location of the vehicle, a planned travel route of the vehicle, a map displayed within the vehicle, and/or a gesture marked by a user on a map. Words spoken within the vehicle are recognized by use of a speech recognition module. The recognizing is dependent upon the geographical information.

Abstract: Embodiments of a dialog system that utilizes a multi-modal input interface for recognizing user input in human-machine interaction (HMI) systems are described. Embodiments include a component that receives user input from a plurality of different user input mechanisms (multi-modal input) and performs certain synchronization and disambiguation processes. The multi-modal input components synchronizes and integrates the information obtained from different modalities, disambiguates the input, and recovers from any errors that might be produced with respect to any of the user inputs. Such a system effectively addresses any ambiguity associated with the user input and corrects for errors in the human-machine interaction.