Abstract: Methods and arrangements for generating a learning graph. A contemplated method includes: utilizing at least one processor to execute instructions to perform the steps of: receiving a proficiency input relating to a student; receiving a target knowledge node, wherein the target knowledge node represents at least one skill the student does not currently possess; determining at least one skill requirement of the at least one skill; identifying at least one path between the proficiency input and the target knowledge node based upon the at least one determined skill requirement; calculating a gap between the proficiency input and the target knowledge node at the at least one identified path; and recommending at least one learning content module based upon the calculated gap.

Abstract: Methods and arrangements for generating a learning graph. A contemplated method includes: utilizing at least one processor to execute instructions to perform the steps of: receiving a proficiency input relating to a student; receiving a target knowledge node, wherein the target knowledge node represents at least one skill the student does not currently possess; determining at least one skill requirement of the at least one skill; identifying at least one path between the proficiency input and the target knowledge node based upon the at least one determined skill requirement; calculating a gap between the proficiency input and the target knowledge node at the at least one identified path; and recommending at least one learning content module based upon the calculated gap.

Abstract: A method, and a system to execute this method is being presented for the identification and separation of sources of an acoustic signal, which signal contains a mixture of multiple simultaneous component signals. The method represents the signal with multiple discrete state-variable sequences and combines acoustic and context level dynamics to achieve the source separation. The method identifies sources by discovering those frames of the signal whose features are dominated by single sources. The signal may be the simultaneous speech of multiple speakers.

Abstract: A method, and a system to execute this method is being presented for the identification and separation of sources of an acoustic signal, which signal contains a mixture of multiple simultaneous component signals. The method represents the signal with multiple discrete state-variable sequences and combines acoustic and context level dynamics to achieve the source separation. The method identifies sources by discovering those frames of the signal whose features are dominated by single sources. The signal may be the simultaneous speech of multiple speakers.

Abstract: Methods and apparatus for automatically deriving multiple phonetic baseforms of a word from a speech utterance of this word are provided in accordance with the present invention. In one embodiment, a method of automatically generating two or more phonetic baseforms from a spoken utterance representing a word includes the steps of: transforming the spoken utterance into a stream of acoustic observations; generating two or more strings of subphone units, wherein each string of subphone units represents a string of subphone units substantially maximizing a log-likelihood of the stream of acoustic observations, and wherein the log-likelihood is computed as a weighted sum of a transition score associated with a transition model and of an acoustic score associated with an acoustic model; and converting the two or more strings of subphone units into two or more phonetic baseforms.

Abstract: A characteristic-specific digitization method and apparatus are disclosed that reduces the error rate in converting input information into a computer-readable format. The input information is analyzed and subsets of the input information are classified according to whether the input information exhibits a specific physical parameter affecting recognition accuracy. If the input information exhibits the specific physical parameter affecting recognition accuracy, the characteristic-specific digitization system recognizes the input information using a characteristic-specific recognizer that demonstrates improved performance for the given physical parameter. If the input information does not exhibit the specific physical parameter affecting recognition accuracy, the characteristic-specific digitization system recognizes the input information using a general recognizer that performs well for typical input information.

Abstract: A system and method for applying a linear transformation to classify and input event. In one aspect, a method for classification comprises the steps of capturing an input event; extracting an n-dimensional feature vector from the input event; applying a linear transformation to the feature vector to generate a pool of projections; utilizing different subsets from the pool of projections to classify the feature vector; and outputting a class identity of the classified feature vector. In another aspect, the step of utilizing different subsets from the pool of projections to classify the feature vector comprises the steps of, for each predefined class, selecting a subset from the pool of projections associated with the class; computing a score for the class based on the associated subset; and assigning, to the feature vector, the class having the highest computed score.

Abstract: The present invention uses acoustic feature transformations, referred to as pattern-specific maximum likelihood transformations (PSMLT), to model the voice print of speakers in either a text dependent or independent mode. Each transformation maximizes the likelihood, when restricting to diagonal models, of the speaker training data with respect to the resulting voice-print model in the new feature space. Speakers are recognized (i.e., identified, verified or classified) by appropriate comparison of the likelihood of the testing data in each transformed feature space and/or by directly comparing transformation matrices obtained during enrollment and testing. It is to be appreciated that the principle of pattern-specific maximum likelihood transformations can be extended to a large number of pattern matching problems and, in particular, to other biometrics besides speech.

Abstract: The present invention provides a new approach to heteroscedastic linear discriminant analysis (HDA) by defining an objective function which maximizes the class discrimination in the projected subspace while ignoring the rejected dimensions. Moreover, we present a link between discrimination and the likelihood of the projected samples and show that HDA can be viewed as a constrained maximum likelihood (ML) projection for a full covariance gaussian model, the constraint being given by the maximization of the projected between-class scatter volume. The present invention also provides that, under diagonal covariance gaussian modeling constraints, applying a diagonalizing linear transformation (e.g., MLLT—maximum likelihood linear transformation) to the HDA space results in an increased classification accuracy.

Abstract: A method is provided for providing high dimensional data. The high dimensional data is linearly transformed into less dependent coordinates, by applying a linear transform of n rows by n columns to the high dimensional data. Each of the coordinates are marginally Gaussianized, the Gaussianization being characterized by univariate Gaussian means, priors, and variances. The transforming and Gaussianizing steps are iteratively repeated until the coordinates converge to a standard Gaussian distribution. The coordinates of all iterations are arranged hierarchically to facilitate data mining. The arranged coordinates are then mined. According to an embodiment of the invention, the transform step includes applying an iterative maximum likelihood expectation maximization (EM) method to the high dimensional data.

Abstract: A characteristic-specific digitization method and apparatus are disclosed that reduces the error rate in converting input information into a computer-readable format. The input information is analyzed and subsets of the input information are classified according to whether the input information exhibits a specific physical parameter affecting recognition accuracy. If the input information exhibits the specific physical parameter affecting recognition accuracy, the characteristic-specific digitization system recognizes the input information using a characteristic-specific recognizer that demonstrates improved performance for the given physical parameter. If the input information does not exhibit the specific physical parameter affecting recognition accuracy, the characteristic-specific digitization system recognizes the input information using a general recognizer that performs well for typical input information.

Abstract: A method is provided for generating a high dimensional density model within an acoustic model for one of a speech and a speaker recognition system. Acoustic data obtained from at least one speaker is transformed into high dimensional feature vectors. The density model is formed to model the feature vectors by a mixture of compound Gaussians with a linear transform, wherein each compound Gaussian is associated with a compound Gaussian prior and models each coordinate of each component of the density model independently by a univariate Gaussian mixture comprising a univariate Gaussian prior, variance, and mean. An iterative expectation maximization (EM) method is applied to the feature vectors. The EM method includes the step of computing an auxiliary function Q of the EM method.

Abstract: A technique to improve the recognition accuracy when transcribing speech data that contains data from a wide range of environments. Input data in many situations contains data from a variety of sources in different environments. Such classes include: clean speech, speech corrupted by noise (e.g., music), non-speech (e.g., pure music with no speech), telephone speech, and the identity of a speaker. A technique is described whereby the different classes of data are first automatically identified, and then each class is transcribed by a system that is made specifically for it. The invention also describes a segmentation algorithm that is based on making up an acoustic model that characterizes the data in each class, and then using a dynamic programming algorithm (the viterbi algorithm) to automatically identify segments that belong to each class. The acoustic models are made in a certain feature space, and the invention also describes different feature spaces for use with different classes.

Abstract: A method and apparatus for acoustic signal processing of speech recognition, the method comprising the following components: 1) Decompose each syllable into two phonemes of comparable length and complexity, the first one being a preme, and the second one being a toneme; 2) Each toneme is assigned a tone value such as high, rising, low, falling, and untoned; 3) No tone value is assigned to premes; 4) Pitch is detected continuously and treated the same way as energy and cepstrals in a Hidden Markov Model to predict the tone of a toneme; 5) The tone of a syllable is defined as the tone of its component toneme.