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

Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Baidu Netcom Science and Technology Co., Ltd.

Tsinghua University

Beijing University of Technology

Baidu Online Network Technology Co., Ltd.

Zhijie Chen

Xiaoyin Fu

Mingxin Liang

Lei Jia

Qiguang Zang

Qi Zhang

Junyao Shao

Zhengxiang Jiang

Liao Zhang

Haifeng Wang

Tao Sun

Xu Chen

Wei Peng Huang

Sheng Qian

Jinfeng Bai

Ying Cao

Kefeng Kenny Zhang

Yongguo Kang

Xiaoyu Zhang

Huanxin Zheng

Muchenxuan Tong

Chunwei Yan

Mingshun Yang

Hanju Guan

Jingwei Bai

Peiyuan Wan

The present invention provides a method for controlling a speed of a polypeptide passing through a nanopore and use thereof in determining an amino acid sequence of a polypeptide. Specifically, the method comprises: conjugating a polynucleotide to the polypeptide to give a polynucleotide-polypeptide conjugate, and applying a voltage across the nanopore in the presence of a polynucleotide binding enzyme to move the conjugate through the nanopore. The polynucleotide binding enzyme controls the movement of the polynucleotide and thereby controls the movement of the conjugated polypeptide in the nanopore, thus controlling the speed of the polypeptide passing through the nanopore.

Method for controlling speed of polypeptide passing through nanopore and use thereof

The present disclosure provides a method of recognizing speech offline, electronic device, and a storage medium, relating to a field of artificial intelligence such as speech recognition, natural language processing, and deep learning. The method may include: decoding speech data to be recognized into a syllable recognition result; transforming the syllable recognition result into a corresponding text as a speech recognition result of the speech data.

Method of recognizing speech offline, electronic device, and storage medium

The present disclosure provides speech recognition and codec methods and apparatuses, an electronic device and a storage medium, and relates to the field of artificial intelligence such as intelligent speech, deep learning and natural language processing. The speech recognition method may include: acquiring an audio feature of to-be-recognized speech; encoding the audio feature to obtain an encoding feature; truncating the encoding feature to obtain continuous N feature fragments, N being a positive integer greater than one; and acquiring, for any one of the feature segments, corresponding historical feature abstraction information, encoding the feature segment in combination with the historical feature abstraction information, and decoding an encoding result to obtain a recognition result corresponding to the feature segment, wherein the historical feature abstraction information is information obtained by feature abstraction of recognized historical feature fragments.

Speech recognition and codec method and apparatus, electronic device and storage medium

A time signal processor based on multiplying phase interpolation circuit is provided. The time signal processor includes a time signal processing circuit, a calibration circuit and a digital logic circuit, and realizes a time input signal-digital output signal conversion process. The time signal processing circuit quantizes an input time signal to obtain a series of digital code output; feedback compensation is carried out on the time signal processing circuit through the calibration circuit; and finally, the digital logic circuit completes final digital output.

Time signal processor based on multiplying phase interpolation circuit

A method for training a speech recognition model, a device and a storage medium, which relate to the field of computer technologies, and particularly to the fields of speech recognition technologies, deep learning technologies, or the like, are disclosed. The method for training a speech recognition model includes: obtaining a fusion probability of each of at least one candidate text corresponding to a speech based on an acoustic decoding model and a language model; selecting a preset number of one or more candidate texts based on the fusion probability of each of the at least one candidate text, and determining a predicted text based on the preset number of one or more candidate texts; and obtaining a loss function based on the predicted text and a standard text corresponding to the speech, and training the speech recognition model based on the loss function.

Method for training speech recognition model, device and storage medium

Proposed are a method and apparatus for speech recognition, and a storage medium. The specific solution includes: obtaining audio data to be recognized; decoding the audio data to obtain a first syllable of a to-be-converted word, in which the first syllable is a combination of at least one phoneme corresponding to the to-be-converted word; obtaining a sentence to which the to-be-converted word belongs and a converted word in the sentence, and obtaining a second syllable of the converted word; encoding the first syllable and the second syllable to generate first encoding information of the first syllable; and decoding the first encoding information to obtain a text corresponding to the to-be-converted word.

Method and apparatus for speech recognition, and storage medium

The present application discloses a method and an apparatus for training a speech spectrum generation model, as well as an electronic device, and relates to the technical field of speech synthesis and deep learning. A specific implementation is as follows: inputting a first text sequence into the speech spectrum generation model to generate an analog spectrum sequence corresponding to the first text sequence, and obtain a first loss value of the analog spectrum sequence according to a preset loss function; inputting the analog spectrum sequence corresponding to the first text sequence into an adversarial loss function model, which is a generative adversarial network model, to obtain a second loss value of the analog spectrum sequence; and training the speech spectrum generation model based on the first loss value and the second loss value.

Method and apparatus for training speech spectrum generation model, and electronic device

A neural network machine translation method comprises: obtaining a to-be-translated source sentence; converting the source sentence into a vector sequence; determining candidate objects corresponding to the vector sequence according to a prefix tree which is pre-obtained and built based on a target sentence database, and determining a target sentence as a translation result according to the candidate objects.

Neural network machine translation method and apparatus

Methods, apparatuses, devices and computer-readable storage media for real-time speech recognition are provided. The method includes: based on an input speech signal, obtaining truncating information for truncating a sequence of features of the speech signal; based on the truncating information, truncating the sequence of features into a plurality of subsequences; and for each subsequence in the plurality of subsequences, obtaining a real-time recognition result through attention mechanism.

Methods, devices and computer-readable storage media for real-time speech recognition

A method and an apparatus for generating a g2p model based on AI are provided. The method includes: during performing a grapheme-to-phoneme conversion training by a neural network on each word in training data, screening nodes in a hidden layer of the neural network randomly according to a preset node ratio so as to obtain retaining nodes for training each word; training each word with a sub-neural network corresponding to the retaining nodes and updating a weight of each retaining node of the sub-neural network; and performing a mean processing on the weights of the retaining nodes of respective sub-neural networks, so as to generate the grapheme-to-phoneme model.

Computer-implemented method and apparatus for generating grapheme-to-phoneme model

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Beijing, China

Zhijie Chen