Abstract: Disclosed herein is a system for tuning hydraulic components of a production digger. The system may comprise a sensor units mounted to the body, boom, stick and bucket of the production digger, and a processor in communication with the sensor units configured to receive signals and determine the rotational velocity and acceleration of various components of the digger around joints of the digger, and display tuning information to a user.

Abstract: The description relates to context-aware management of satellite images. One example can track a satellite relative to locations on earth, ground stations, and other satellites and identify parameters associated with the tracked satellite. The example can manage images captured by the satellite for individual locations based upon the identified parameters.

Abstract: Software that performs the following steps: (i) receiving data from a first database and data from a second database, (ii) identifying a training subset and a test subset from the received data; (iii) generating a first graphical model using data from the training subset; (iv) generating a second graphical model using data from the training subset; (v) determining respective weights for the first graphical model and the second graphical model by using an expectation maximization method on data from the test subset; (vi) generating a third graphical model by interpolating at least the first graphical model and the second graphical model using their respectively determined weights; and (vii) defining one or more links between the data from the first database and the data from the second database using the third graphical model.

Abstract: Software that performs the following steps: (i) receiving a first set of observed data pertaining to healthcare events, the first set of observed data including a subset of patient care event data pertaining to patient care events and a subset of prescription data pertaining to prescription events; (ii) generating a graphical model representing a probabilistic relationship between the patient care event data and the prescription data, the graphical model including a set of latent variable(s) estimated from the first set of observed data using an expectation maximization method; (iii) receiving a second set of observed data pertaining to healthcare events associated with a healthcare provider; and (iv) computing, using a dynamic programming approach, a first prescription score for the healthcare provider relating to a computed probability under the generated graphical model of at least one prescription event of the second set of observed data.

Abstract: Techniques disclosed herein include using a Maximum A Posteriori (MAP) adaptation process that imposes sparseness constraints to generate acoustic parameter adaptation data for specific users based on a relatively small set of training data. The resulting acoustic parameter adaptation data identifies changes for a relatively small fraction of acoustic parameters from a baseline acoustic speech model instead of changes to all acoustic parameters. This results in user-specific acoustic parameter adaptation data that is several orders of magnitude smaller than storage amounts otherwise required for a complete acoustic model. This provides customized acoustic speech models that increase recognition accuracy at a fraction of expected data storage requirements.

Abstract: Techniques disclosed herein include using a Maximum A Posteriori (MAP) adaptation process that imposes sparseness constraints to generate acoustic parameter adaptation data for specific users based on a relatively small set of training data. The resulting acoustic parameter adaptation data identifies changes for a relatively small fraction of acoustic parameters from a baseline acoustic speech model instead of changes to all acoustic parameters. This results in user-specific acoustic parameter adaptation data that is several orders of magnitude smaller than storage amounts otherwise required for a complete acoustic model. This provides customized acoustic speech models that increase recognition accuracy at a fraction of expected data storage requirements.

Abstract: Techniques disclosed herein include using a Maximum A Posteriori (MAP) adaptation process that imposes sparseness constraints to generate acoustic parameter adaptation data for specific users based on a relatively small set of training data. The resulting acoustic parameter adaptation data identifies changes for a relatively small fraction of acoustic parameters from a baseline acoustic speech model instead of changes to all acoustic parameters. This results in user-specific acoustic parameter adaptation data that is several orders of magnitude smaller than storage amounts otherwise required for a complete acoustic model. This provides customized acoustic speech models that increase recognition accuracy at a fraction of expected data storage requirements.

Abstract: Techniques disclosed herein include systems and methods for privacy-sensitive training data collection for updating acoustic models of speech recognition systems. In one embodiment, the system locally creates adaptation data from raw audio data. Such adaptation can include derived statistics and/or acoustic model update parameters. The derived statistics and/or updated acoustic model data can then be sent to a speech recognition server or third-party entity. Since the audio data and transcriptions are already processed, the statistics or acoustic model data is devoid of any information that could be human-readable or machine readable such as to enable reconstruction of audio data. Thus, such converted data sent to a server does not include personal or confidential information. Third-party servers can then continually update speech models without storing personal and confidential utterances of users.

Abstract: Access is obtained to a large reference acoustic model for automatic speech recognition. The large reference acoustic model has L states modeled by L mixture models, and the large reference acoustic model has N components. A desired number of components Nc, less than N, to be used in a restructured acoustic model derived from the reference acoustic model, is identified. The desired number of components Nc is selected based on a computing environment in which the restructured acoustic model is to be deployed. The restructured acoustic model also has L states. For each given one of the L mixture models in the reference acoustic model, a merge sequence is built which records, for a given cost function, sequential mergers of pairs of the components associated with the given one of the mixture models. A portion of the Nc components is assigned to each of the L states in the restructured acoustic model.

Abstract: A computer implemented method, system and/or computer program product confirm an orally entered address to a mobile navigation device. The mobile navigation device receives a global positioning system (GPS) root address component from a GPS. The GPS root address component is a text name of a root location at which a mobile navigation device is currently located. The mobile navigation device receives an orally entered address that comprises an oral root address component and an oral subunit component of the oral root address component. In response to the converted root address component matching the GPS root address component, the orally entered address is partitioned into the oral subunit component and the oral root address component, and any additional speech-to-text conversion of the orally entered address after the oral root address component is terminated.

Abstract: A method, system, and computer program for class detection and time mediated averaging of class dependent models. A technique is described to take advantage of gender information in training data and how obtain female, male, and gender independent models from this information. By using a probability value to average male and female Gaussian Mixture Models (GMMs), dramatic deterioration in cross gender decoding performance is avoided.

Abstract: Access is obtained to a large reference acoustic model for automatic speech recognition. The large reference acoustic model has L states modeled by L mixture models, and the large reference acoustic model has N components. A desired number of components Nc, less than N, to be used in a restructured acoustic model derived from the reference acoustic model, is identified. The desired number of components Nc is selected based on a computing environment in which the restructured acoustic model is to be deployed. The restructured acoustic model also has L states. For each given one of the L mixture models in the reference acoustic model, a merge sequence is built which records, for a given cost function, sequential mergers of pairs of the components associated with the given one of the mixture models. A portion of the Nc components is assigned to each of the L states in the restructured acoustic model.

Abstract: A computer implemented method, system and/or computer program product confirm an orally entered address to a mobile navigation device. The mobile navigation device receives a global positioning system (GPS) root address component from a GPS. The GPS root address component is a text name of a root location at which a mobile navigation device is currently located. The mobile navigation device receives an orally entered address that comprises an oral root address component and an oral subunit component of the oral root address component. In response to the converted root address component matching the GPS root address component, the orally entered address is partitioned into the oral subunit component and the oral root address component, and any additional speech-to-text conversion of the orally entered address after the oral root address component is terminated.

Abstract: The present invention relates to a nutriceutical and/or pharmaceutical composition for oral use containing a calcium-containing compound. The composition is in the form of tablets that are designed so that they have an acceptable taste and mouthfeel, whereby the tablets are chewable or suckable, and at the same time the tablets fulfil the requirements with respect to technical properties in order to ensure that the tablets can be dispensed by means of a dose-dispensing machine. In a preferred embodiment, the tablet comprises calcium carbonate and sorbitol with a mean particle size of 38 or 110 microns. In another embodiment, the tablets comprise calcium carbonate and Vitamin D as active ingredients.

Abstract: A dry manufacturing process for the production of a pharmaceutical formulation of a benzimidazole and an alkaline substance is described. A tablet is compressed directly from a dry powder or a dry particulate matter avoiding any liquid or excipient conventionally used as a wet binder. The manufacturing process has the advantage of being simple and cost efficient. At the same time an expensive drying step is superfluous. The resulting pharmaceutical formulation has a good stability and a good dissolution profile.

Abstract: A method, system, and computer program for class detection and time mediated averaging of class dependent models. A technique is described to take advantage of gender information in training data and how obtain female, male, and gender independent models from this information. By using a probability value to average male and female Gaussian Mixture Models (GMMs), dramatic deterioration in cross gender decoding performance is avoided.

Abstract: A parametric family of multivariate density functions formed by mixture models from univariate functions of the type exp(−|x|&bgr;) for modeling acoustic feature vectores are used in automatic recognition of speech. The parameter &bgr; is used to measure the non-Gaussian nature of the data. &bgr; is estimated from the input data using a maximum likelihood criterion. There is a balance between &bgr; and the number of data points that must be satisfied for efficient estimation.

Abstract: Improvements in speech recognition systems are achieved by considering projections of the high dimensional data on lower dimensional subspaces, subsequently by estimating the univariate probability densities via known univariate techniques, and then by reconstructing the density in the original higher dimensional space from the collection of univariate densities so obtained. The reconstructed density is by no means unique unless further restrictions on the estimated density are imposed. The variety of choices of candidate univariate densities as well as the choices of subspaces on which to project the data including their number further add to this non-uniqueness. Probability density functions are then considered that maximize certain optimality criterion as a solution to this problem. Specifically, those probability density function's that either maximize the entropy functional, or alternatively, the likelihood associated with the data are considered.

Abstract: A nonparametric family of density functions formed by histogram estimators for modeling acoustic vectors are used in automatic recognition of speech. A Gaussian kernel is set forth in the density estimator. When the densities are found for all the basic sounds in a training stage, an acoustic vector is assigned to a phoneme label corresponding to the highest likelihood for the basis of the decoding of acoustic vectors into text.