Abstract: A junction box controller controls a junction box including a circuit for vehicle travel that supplies power from a battery for vehicle travel to an electrical component for vehicle travel and a charging circuit including a positive electrode charging relay and an negative electrode charging relay that are for use in coupling and decoupling the battery for vehicle travel to and from an external charging power source in a state in which the positive electrode charging relay and the negative electrode charging relay are coupled to the circuit for vehicle travel. The junction box controller includes a temperature sensor that detects a temperature of the circuit for vehicle travel, and a controller that, when the temperature of the circuit for vehicle travel reaches or exceeds a preset threshold, controls and switches on either one of the positive electrode charging relay and the negative electrode charging relay.

Abstract: A technique for detecting a music segment in an audio signal is disclosed. A time window is set for each section in an audio signal. A maximum and a statistic of the audio signal within the time window are calculated. A density index is computed for the section using the maximum and the statistic. The density index is a measure of the statistic relative to the maximum. The section is estimated as a music segment based, at least in part, on a condition with respect to the density index.

Abstract: A computer-implemented method for training a front-end neural network (“front-end NN”) and a back-end neural network (“back-end NN”) is provided. The method includes combining the back-end neural network with the front-end neural network to form a joint layer to thereby generate a combined neural network. The method also includes training the combined neural network for a speech recognition with a set of utterances as training data, with the joint layer having a plurality of frames and each frame having a plurality of bins, and where one or more specific units in each frame are dropped during the training, each of the specific units being selected randomly or based on a bin number to which the respective unit is set within its frame, with the specific units corresponding to one or more common frequency bands.

Abstract: A first user request which specifies a target document set wherein a first subset of the documents is flagged by a user. A primary flag table is created for the target document set. A first document subset is created matching the first user request. It is determined whether a number of flagged documents exceeds a first threshold. If so, a secondary flag table is created for the first document subset and flag data corresponding to the first document subset is stored in the secondary flag table. The flag data in the secondary flag table is merged into the primary flag table.

Abstract: A technique for data augmentation for speech data is disclosed. Original speech data including a sequence of feature frames is obtained. A partially prolonged copy of the original speech data is generated by inserting one or more new frames into the sequence of the feature frames. The partially prolonged copy is output as augmented speech data for training an acoustic model for training an acoustic model.

Abstract: A method is provided for training a neural network-based (NN-based) acoustic model. The method includes receiving, by a processor, the neural network-based (NN-based) acoustic model, trained by a one-hot scheme and having an input layer, a set of middle layers, and an original output layer. At least each of the middle layers subsequent to a first one of the middle layers have trained parameters. The method further includes stacking, by the processor, a new output layer on the original output layer of the NN-based acoustic model to form a new NN-based acoustic model. The new output layer has a same size as the original output layer. The method also includes retraining, by the processor, only the new output layer and the original output layer of the new NN-based acoustic model in the one-hot scheme, with the trained parameters of middle layers subsequent to at least the first one being fixed.

Abstract: Natural Language Processing (NLP) is performed on a corpus using a processor and a memory to extract a set of facets corresponding to a dimension in a set of dimensions. Using a score threshold, a subset of the set of facets is selected where each facet in the set of facets has a corresponding score relative to the corpus. A subsequent query is formed by increasing a complexity of a previous query using a facet in the subset of facets. The subsequent query is executed on at least a portion of the corpus. The documents in a new result set are ranked, the new result set being in response to executing the subsequent query. An output is produced from the new result set, which includes a ranking of that subset of documents whose ranks have changed by more than a threshold rank distance from the corresponding ranks in the corpus.

Abstract: Fusion of neural networks is performed by obtaining a first neural network and a second neural network. The first and the second neural networks are the result of a parent neural network subjected to different training. A similarity score is calculated of a first component of the first neural network and a corresponding second component of the second neural network. An interpolation weight is determined for the first and the second components by using the similarity score. A neural network parameter of the first component is updated based on the interpolation weight and a corresponding neural network parameter of the second component to obtain a fused neural network.

Abstract: A method for transferring acoustic knowledge of a trained acoustic model (AM) to a neural network (NN) includes reading, into memory, the NN and the AM, the AM being trained with target domain data, and a set of training data including a set of phoneme data, the set of training data being data obtained from a domain different from a target domain for the target domain data, inputting training data from the set of training data into the AM, calculating one or more posterior probabilities of context-dependent states corresponding to phonemes in a phoneme class of a phoneme to which each frame in the training data belongs, and generating a posterior probability vector from the one or more posterior probabilities, as a soft label for the NN, and inputting the training data into the NN and updating the NN, using the soft label.

Abstract: A computer-implemented method is provided. The computer-implemented method is performed by a speech recognition system having at least a processor. The method further includes performing a speech recognition operation on the audio signal data to decode the audio signal data into a textual representation based on the estimated sound identification information from a neural network having periodic indications and components of a frequency spectrum of the audio signal data inputted thereto. The neural network includes a plurality of fully-connected network layers having a first layer that includes a plurality of first nodes and a plurality of second nodes. The method further comprises training the neural network by initially isolating the periodic indications from the components of the frequency spectrum in the first layer by setting weights between the first nodes and a plurality of input nodes corresponding to the periodic indications to 0.

Abstract: Acoustic change is detected by a method including preparing a first Gaussian Mixture Model (GMM) trained with first audio data of first speech sound from a speaker at a first distance from an audio interface and a second GMM generated from the first GMM using second audio data of second speech sound from the speaker at a second distance from the audio interface; calculating a first output of the first GMM and a second output of the second GMM by inputting obtained third audio data into the first GMM and the second GMM; and transmitting a notification in response to determining at least that a difference between the first output and the second output exceeds a threshold. Each Gaussian distribution of the second GMM has a mean obtained by shifting a mean of a corresponding Gaussian distribution of the first GMM by a common channel bias.

Abstract: Embodiments are directed to a system, computer program product, and method for text mining, and dynamic facet and facet value management and application to a document collection. Two or more words from a first document collection are extracted, with the extracted words being associated with an applied annotation. At least one word is selected from the extracted words, designated as a facet, and a value is selectively added to the facet. An analysis of the added value is dynamically performed, and a dictionary with the annotation, facet, and values is constructed and the dictionary is applied to the document collection. A targeted list of documents is returned from the dictionary application to the document collection.

Abstract: Embodiments are directed to a system, computer program product, and method for dynamic facet dictionary management. As one or more annotations are applied to a document collection, electronic text and associated facets are identified. Additional facets and facet values are identified and selectively applied to a knowledge base. A dictionary comprised of facets and associated facet values is constructed from the selective application. Application of the dictionary to the knowledge base identifies and returns a targeted document collection. Accordingly, facet mining and dictionary construction are dynamically applied to the knowledge base.

Abstract: A substrate includes: a wiring substrate; a lower plate disposed below the wiring substrate; a canted coil spring disposed between the lower plate and the wiring substrate; an electronic component package disposed above the wiring substrate; a sheet disposed between the wiring substrate and the electronic component package and including a plurality of connection members that connects a plurality of first electrodes provided on an upper surface of the wiring substrate and a plurality of second electrodes provided on a lower surface of the electronic component package; an upper plate disposed on the electronic component package; and a coupling member that couples the lower plate and the upper plate, wherein the lower plate, the canted coil spring, the wiring substrate, the sheet, the electronic component package, and the upper plate are laminated and fixed in this order by coupling the lower plate and the upper plate by the coupling member.

Abstract: A method, computer system, and a computer program product for generating a plurality of voice data having a particular speaking style is provided. The present invention may include preparing a plurality of original voice data corresponding to at least one word or at least one phrase is prepared. The present invention may also include attenuating a low frequency component and a high frequency component in the prepared plurality of original voice data. The present invention may then include reducing power at a beginning and an end of the prepared plurality of original voice data. The present invention may further include storing a plurality of resultant voice data obtained after the attenuating and the reducing.

Abstract: A method for learning a neural network having a plurality of filters for extracting local features performed by a computing device is disclosed. The computing device calculates a plurality of projection parameter sets by analyzing one or more training data. The plurality of the projection parameter sets define a projection of each training data into a new space and each projection parameter set has a same size as the filters in the neural network. At least part of the plurality of the projection parameter sets is set as initial parameters of at least part of the plurality of the filters in the neural network for training.

Abstract: A technique for detecting a signal tone in an audio signal is disclosed. A determination is made as to whether a peak modulation frequency in the audio signal is in a specific range or not to obtain a determination result. A measure regarding a modulation spectrum of the audio signal is calculated. The measure is calculated based on at least components of the modulation spectrum above a specific limit of modulation frequency. By using the determination result and the measure regarding the modulation spectrum, a judgement is done as to whether the audio signal contains a signal tone or not.

Abstract: A hybrid vehicle driving system includes: a generator; a motor; a case which accommodates the generator and the motor; and a power control unit for controlling the generator and the motor, the generator and the motor being disposed side by side on a same axis within the case. The power control unit is mounted on the case by connecting a unit-side generator connector and a unit-side motor connector which are provided on a bottom surface of the power control unit with a case-side generator connector and a case-side motor connector which are disposed on the case, directly and respectively. The case is fixed to a vehicle framework member via a mount member, and a fixing point where the case and the mount member are fixed together is disposed near the case-side generator connector and the case-side motor connector.

Abstract: A foam molding method includes: sealing a core material and a skin material with a sealing part; and molding a foam layer on the inside of the sealing part. The sealing of the core material and the skin material with the sealing part includes sealing the core material and the skin material so that the sealing part includes a self sealing part for which an edge part of the skin material is housed in a recess provided in the core material, a clamp sealing part sealed by clamping the core material and the edge part of the skin material by foam molding molds, and a crimp sealing part arranged in a switchover part between the clamp sealing part and the self sealing part, and sealed by crimping the edge part of the skin material to the core material.

Abstract: A computer-implemented method and system for clustering facets on a two-dimensional facet cube for text mining. The method and system performs text mining based on facets to analyze unstructured data in one or more documents by generating a two-dimensional facet cube that is a correlation matrix for one or more facets associated with a set of one or more of the documents; grouping one or more of the facets in the correlation matrix into at least one cluster; calculating a center for the cluster; and identifying facets that are located near the calculated center of the cluster as being representative of the cluster.