Tencent Technology (shenzhen) Company Limited

Boe Technology Group Co., Ltd.

Tencent America LLC

Beijing Boe Optoelectronics Technology Co., Ltd.

Beijing Boe Display Technology Co., Ltd.

Baidu Online Network Technology (beijing) Co., Ltd.

Beijing Didi Infinity Technology and Development Co., Ltd.

Bitmain Technologies Limited

Huawei Marine Networks Co., Ltd.

Baidu Online Network Technology Co., Ltd.

B.e.a Electronics (beijing) Co., Ltd.

Dan Su

Jun Wang

Dong Yu

Chao Weng

Chengzhu Yu

Jia Cui

Lianwu Chen

Guangsen Wang

Jie Chen

Meng Yu

Min Luo

Shuo Li

Liang Zhang

Jieqiong Wang

Zongze He

Jianguang Yang

Zhipeng Feng

Qi Chen

Shiyin Kang

Na Hu

Tianxiao Fu

Yulu Zhang

Jian Li

Jun Wu

Tao Zhou

Yi Gao

Yong Xu

Jimeng Zheng

Yong Guan

Weiwei Li

Deyi Tuo

Kaiyu Jiang

Caisheng Ouyang

Wenhai Cui

Yanmin Qian

Wingyip Lam

Kuongchi Lei

Mu Wang

Micree Zhan

Guozhong Wang

Wing Yip Lam

Shixiong Zhang

Ji Meng Zheng

Jingliang Bai

Lin Luo

Xixin Wu

Haikang Liu

Guoyao Zhou

Yonggang Sun

Rongzhi Gu

Huirong Huang

Zhiyi Duan

Mingjie Jin

Zhenling Zhu

Zhao Yin

Jixiang Liu

Yuhe Xu

A method of training an audio separation network is provided. The method includes obtaining a first separation sample set, the first separation sample set including at least two types of audio with dummy labels, obtaining a first sample set by performing interpolation on the first separation sample set based on perturbation data, obtaining a second separation sample set by separating the first sample set using an unsupervised network, determining losses of second separation samples in the second separation sample set, and adjusting network parameters of the unsupervised network based on the losses of the second separation samples, such that a first loss of a first separation result outputted by an adjusted unsupervised network meets a convergence condition.

Training method and device for audio separation network, audio separation method and device, and medium

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.

Chip heat dissipation structure, chip structure, circuit board and supercomputing device

A data processing method based on simultaneous interpretation, applied to a server in a simultaneous interpretation system, including: obtaining audio transmitted by a simultaneous interpretation device; processing the audio by using a simultaneous interpretation model to obtain an initial text; transmitting the initial text to a user terminal; receiving a modified text fed back by the user terminal, the modified text being obtained after the user terminal modifies the initial text; and updating the simultaneous interpretation model according to the initial text and the modified text.

Data processing method based on simultaneous interpretation, computer device, and storage medium

This application discloses a multi-sound area-based speech detection method and related apparatus, and a storage medium, which is applied to the field of artificial intelligence. The method includes: obtaining sound area information corresponding to N sound areas including multiple users speaking simultaneously; generating a control signal corresponding to each target detection sound area according to user information corresponding to the target detection sound area; processing multi-user speech input signals by using the control signals, to obtain a speech output signal corresponding to each target detection sound area; generating a speech detection result of the target detection sound area according to the speech output signal corresponding to the target detection sound area; and selecting, among the multiple users, a main speaker based on the user information, the speech output signals and speech detection results of multiple users in the N sound areas.

Multi-register-based speech detection method and related apparatus, and storage medium

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.

Method for speech recognition based on language adaptivity and related apparatus

This application relates to a speech synthesis method and apparatus, a model training method and apparatus, and a computer device. The method includes: obtaining to-be-processed linguistic data; encoding the linguistic data, to obtain encoded linguistic data; obtaining an embedded vector for speech feature conversion, the embedded vector being generated according to a residual between synthesized reference speech data and reference speech data that correspond to the same reference linguistic data; and decoding the encoded linguistic data according to the embedded vector, to obtain target synthesized speech data on which the speech feature conversion is performed. The solution provided in this application can prevent quality of a synthesized speech from being affected by a semantic feature in a mel-frequency cepstrum.

Voice synthesis method, model training method, device and computer device

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.

Speech-driven animation method and apparatus based on artificial intelligence

A mixed speech recognition method, a mixed speech recognition apparatus, and a computer-readable storage medium are provided. The mixed speech recognition method includes: monitoring an input of speech input and detecting an enrollment speech and a mixed speech; acquiring speech features of a target speaker based on the enrollment speech; and determining speech belonging to the target speaker in the mixed speech based on the speech features of the target speaker. The enrollment speech includes preset speech information, and the mixed speech is non-enrollment speech inputted after the enrollment speech.

Mixed speech recognition method and apparatus, and computer-readable storage medium

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.

Multi-task training architecture and strategy for attention-based speech recognition system

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. The method includes: transforming one channel component of a multi-channel multi-sound source mixed audio signal into a single-channel multi-sound source mixed audio representation in a feature space; performing a two-dimensional dilated convolution on the multi-channel multi-sound source mixed audio signal to extract inter-channel features; performing a feature fusion on the single-channel multi-sound source mixed audio representation and the inter-channel features; estimating respective weights of sound sources in the single-channel multi-sound source mixed audio representation based on a fused multi-channel multi-sound source mixed audio feature; obtaining respective representations of the plurality of sound sources according to the single-channel multi-sound source mixed audio representation and the respective weights; and transforming the respective representations of the sound sources into respective audio signals of the plurality of sound sources.

Inter-channel feature extraction method, audio separation method and apparatus, and computing device

A speech separation model training method and apparatus, a computer-readable storage medium, and a computer device are provided, the method including: obtaining first audio and second audio, the first audio including target audio and having corresponding labeled audio, and the second audio including noise audio. obtaining an encoding model, an extraction model, and an initial estimation model; performing unsupervised training on the encoding model, the extraction model, and the estimation model according to the second audio, and adjusting model parameters of the extraction model and the estimation model; performing supervised training on the encoding model and the extraction model according to the first audio and the labeled audio corresponding to the first audio, and adjusting a model parameter of the encoding model; continuously performing the unsupervised training and the supervised training, so that the unsupervised training and the supervised training overlap, and the training is not finished until a training stop condition is met.

Speech separation model training method and apparatus, storage medium and computer device

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.

Audio recognition method and system and machine device

A far-field pickup device including a device body and a microphone pickup unit is provided. The microphone pickup unit is configured to collect user speech and an echo of a first sound signal output by the device body, and transmit, to the device body, a signal obtained through digital conversion of the collected user speech and the echo. The device body includes a signal playback source, a synchronizing signal generator, a horn, a delay determining unit, and an echo cancellation unit configured to perform echo cancellation on the signal transmitted by the microphone pickup unit to obtain a collected human voice signal.

Far-field pickup device and method for collecting voice signal in far-field pickup device

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.

Artificial intelligence-based wakeup word detection method and apparatus, device, and medium

A method and apparatus are provided that analyzing sequence-to-sequence data, such as sequence-to-sequence speech data or sequence-to-sequence machine translation data for example, by minimum Bayes risk (MBR) training a sequence-to-sequence model and within introduction of applications of softmax smoothing to an N-best generation of the MBR training of the sequence-to-sequence model.

N-best softmax smoothing for minimum bayes risk training of attention based sequence-to-sequence models

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Beijing, China

Dan Su