Abstract: Methods and apparatuses are provided for performing sequence to sequence (Seq2Seq) speech recognition training performed by at least one processor. The method includes acquiring a training set comprising a plurality of pairs of input data and target data corresponding to the input data, encoding the input data into a sequence of hidden states, performing a connectionist temporal classification (CTC) model training based on the sequence of hidden states, performing an attention model training based on the sequence of hidden states, and decoding the sequence of hidden states to generate target labels by independently performing the CTC model training and the attention model training.

Abstract: Methods and apparatuses are provided for performing sequence to sequence (Seq2Seq) speech recognition training performed by at least one processor. The method includes acquiring a training set comprising a plurality of pairs of input data and target data corresponding to the input data, encoding the input data into a sequence of hidden states, performing a connectionist temporal classification (CTC) model training based on the sequence of hidden states, performing an attention model training based on the sequence of hidden states, and decoding the sequence of hidden states to generate target labels by independently performing the CTC model training and the attention model training.

Abstract: Embodiments of this application disclose a speech-driven animation method and apparatus based on artificial intelligence (AI). The method includes obtaining a first speech, the first speech comprising a plurality of speech frames; determining linguistics information corresponding to a speech frame in the first speech, the linguistics information being used for identifying a distribution possibility that the speech frame in the first speech pertains to phonemes; determining an expression parameter corresponding to the speech frame in the first speech according to the linguistics information; and enabling, according to the expression parameter, an animation character to make an expression corresponding to the first speech.

Abstract: A wire comprising a wire core with a surface, the wire core having a coating layer superimposed on its surface, wherein the wire core itself consists of: (a) pure silver consisting of (a1) silver in an amount in the range of from 99.99 to 100 wt.-% and (a2) further components in a total amount of from 0 to 100 wt.-ppm or (b) doped silver consisting of (b1) silver in an amount in the range of from >99.49 to 99.997 wt.-%, (b2) at least one doping element selected from the group consisting of calcium, nickel, platinum, palladium, gold, copper, rhodium and ruthenium in a total amount of from 30 to <5000 wt.-ppm and (b3) further components in a total amount of from 0 to 100 wt.-ppm, or (c) a silver alloy consisting of (c1) silver in an amount in the range of from 89.99 to 99.5 wt.-%, (c2) at least one alloying element selected from the group consisting of nickel, platinum, palladium, gold, copper, rhodium and ruthenium in a total amount in the range of from 0.5 to 10 wt.

Abstract: This application discloses an artificial intelligence-based (AI-based) wakeup word detection method performed by a computing device. The method includes: constructing, by using a preset pronunciation dictionary, at least one syllable combination sequence for self-defined wakeup word text inputted by a user; obtaining to-be-recognized speech data, and extracting speech features of speech frames in the speech data; inputting the speech features into a pre-constructed deep neural network (DNN) model, to output posterior probability vectors of the speech features corresponding to syllable identifiers; determine a target probability vector from the posterior probability vectors according to the syllable combination sequence; and calculate a confidence according to the target probability vector, and determine that the speech frames include the wakeup word text when the confidence is greater than or equal to a threshold.

Abstract: A speech keyword recognition method includes: obtaining first speech segments based on a to-be-recognized speech signal; obtaining first probabilities respectively corresponding to the first speech segments by using a preset first classification model. A first probability of a first speech segment is obtained from probabilities of the first speech segment respectively corresponding to pre-determined word segmentation units of a pre-determined keyword.

Abstract: This application provides a speech recognition and apparatus and a neural network training method and apparatus, and relates to the field of Artificial Intelligence (AI) technologies. The neural network training method is performed by an electronic device and includes: obtaining sample data, the sample data including a mixed speech spectrum and a labeled phoneme thereof; extracting a target speech spectrum from the mixed speech spectrum by using a first subnetwork; adaptively transforming the target speech spectrum by using a second subnetwork, to obtain an intermediate transition representation; performing phoneme recognition based on the intermediate transition representation by using a third subnetwork; and updating parameters of the first subnetwork, the second subnetwork, and the third subnetwork according to a result of the phoneme recognition and the labeled phoneme.

Abstract: A silicone adhesive comprises a polysiloxane with a hydroxyl group and/or a hydrolyzable group bonded with a silicon atom, a catalyst, a cross-linking agent, and a filler, wherein the filler comprises at least one rubber filler. The silicone adhesive not only reduces costs, but also maintains the original performance of the adhesive, reduces the hardness, and improves the moisture and heat aging resistance of the adhesive.

Abstract: This application relates to a method of extracting an inter channel feature from a multi-channel multi-sound source mixed audio signal performed at a computing device.

Abstract: A method for gaze estimation. The method includes processing training data and determining one or more local-learning base gaze estimation model based on the training data. The local-learning base gaze estimation model(s) can be used for determining one or both of: 2D gaze points in a scene image and 3D gaze points in scene camera coordinates.

Abstract: Glycosides are used in the preparation of drugs for preventing and treating diabetes complications. The compound can reduce levels of urea and creatinine, retard the extent of mesangium or mesangium cell proliferation, and has a certain protective effect on the kidneys. The compound can significantly reduce the degree of degeneration in the retinal ganglion cell layer and has an effect of slowing degeneration in the retinal ganglion cell layer of animals.

Abstract: Embodiments of the present invention provide a speech signal processing model training method, an electronic device and a storage medium. The embodiments of the present invention determines a target training loss function based on a training loss function of each of one or more speech signal processing tasks; inputs a task input feature of each speech signal processing task into a starting multi-task neural network, and updates model parameters of a shared layer and each of one or more task layers of the starting multi-task neural network corresponding to the one or more speech signal processing tasks by minimizing the target training loss function as a training objective, until the starting multi-task neural network converges, to obtain a speech signal processing model.

Abstract: Embodiments of the present application relates to a chip heat dissipation structure, a chip structure, a circuit board, and a supercomputing device, and the chip heat dissipation structure includes: a plating layer covering a wafer of the chip; where the plating layer includes a first metal layer, a second metal layer, and a third metal layer sequentially arranged. Three metal layers are added on a top of the chip by physical sputtering, so that a heat sink can be welded on the metal layers by a solder layer, and then the heat sink is fixed on the top of the chip; a main component of the solder layer is metal tin, and the metal layer have a higher thermal conductivity than an epoxy adhesive material mounted on a conventional heat sink.

Abstract: A method for speech recognition based on language adaptivity comprises obtaining voice data of a user. The method also comprises extracting, based on the obtained voice data, a phoneme feature representing pronunciation phoneme information. The phoneme feature is input to a pre-trained language discrimination model that is pre-trained based on a multilingual corpus. A language discrimination result corresponding to the phoneme feature and in accordance with the language discrimination model is obtained. The method also comprises obtaining a speech recognition result of the voice data based on a language acoustic model of a language corresponding to the language discrimination result. The method further comprises determining a speech recognition result of the voice data based on a language acoustic model of a language corresponding to the language discrimination result.

Abstract: A neural network training method is provided. The method includes obtaining an audio data stream, performing, for different audio data of each time frame in the audio data stream, feature extraction in each layer of a neural network, to obtain a depth feature outputted by a corresponding time frame, fusing, for a given label in labeling data, an inter-class confusion measurement index and an intra-class distance penalty value relative to the given label in a set loss function for the audio data stream through the depth feature, and updating a parameter in the neural network by using a loss function value obtained through fusion.

Abstract: A wire comprising a wire core with a surface, the wire core having a coating layer superimposed on its surface, wherein the wire core itself consists of: (a) pure silver consisting of (a1) silver in an amount in the range of from 99.99 to 100 wt.-% and (a2) further components in a total amount of from 0 to 100 wt.-ppm or (b) doped silver consisting of (b1) silver in an amount in the range of from >99.49 to 99.997 wt.-%, (b2) at least one doping element selected from the group consisting of calcium, nickel, platinum, palladium, gold, copper, rhodium and ruthenium in a total amount of from 30 to <5000 wt.-ppm and (b3) further components in a total amount of from 0 to 100 wt.-ppm, or (c) a silver alloy consisting of (c1) silver in an amount in the range of from 89.99 to 99.5 wt.-%, (c2) at least one alloying element selected from the group consisting of nickel, platinum, palladium, gold, copper, rhodium and ruthenium in a total amount in the range of from 0.5 to 10 wt.

Abstract: An adjustable hyperspectral detection chip enhanced by a multi-resonance plasmonic mechanism. The detection chip consists of an array of metal nanonail resonator detection units. Each detection unit (1) comprises: a bottom electrode (2), a semiconductor material layer (3), a spacer layer (4), a nanonail array (5), a control material layer (6), a top electrode (7), a peripheral control signal (8), and a driving circuit (9). The positional relationship from top to bottom is the top electrode (7), the control material layer (6), the nanonail array (5), the spacer layer (4), the semiconductor material layer (3), and the bottom electrode (2). The nanonail array (5) is loaded inside the control material layer (6), and the peripheral control signal (8) and the driving circuit (9) are connected to both sides of the control material layer (6).

Abstract: The present disclosure relates to a method, apparatus, and system for speaker verification. The method includes: acquiring an audio recording; extracting speech signals from the audio recording; extracting features of the extracted speech signals; and determining whether the extracted speech signals represent speech by a predetermined speaker based on the extracted features and a speaker model trained with reference voice data of the predetermined speaker.

Abstract: A speech recognition method, a speech recognition apparatus, and a method and an apparatus for training a speech recognition model are provided. The speech recognition method includes: recognizing a target word speech from a hybrid speech, and obtaining, as an anchor extraction feature of a target speech, an anchor extraction feature of the target word speech based on the target word speech; obtaining a mask of the target speech according to the anchor extraction feature of the target speech; and recognizing the target speech according to the mask of the target speech.

Abstract: An adjustable hyperspectral detection chip enhanced by a multi-resonance plasmonic mechanism. The detection chip consists of an array of metal nanonail resonator detection units. Each detection unit (1) comprises: a bottom electrode (2), a semiconductor material layer (3), a spacer layer (4), a nanonail array (5), a control material layer (6), a top electrode (7), a peripheral control signal (8), and a driving circuit (9). The positional relationship from top to bottom is the top electrode (7), the control material layer (6), the nanonail array (5), the spacer layer (4), the semiconductor material layer (3), and the bottom electrode (2). The nanonail array (5) is loaded inside the control material layer (6), and the peripheral control signal (8) and the driving circuit (9) are connected to both sides of the control material layer (6).