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11.发明申请公开(公告)号：US20220004526A1
公开(公告)日：2022-01-06
申请号：US17480294
申请日：2021-09-21
Inventor： Liujie ZHANG , Yamei LI , Huihuang ZHENG , Hongyu LIU , Xiang LAN , Dianhai YU , Yanjun MA , Tian WU , Haifeng WANG
Abstract: According to exemplary embodiments of the present disclosure, there is provided a method and apparatus of converting a schema in a deep learning framework, and a computer storage medium. The method of converting the schema in the deep learning framework includes: updating a first schema, based on first syntax elements in the first schema and a context relationship between the first syntax elements in the first schema, so as to obtain an updated first schema; generating second syntax elements corresponding to updated first syntax elements in the updated first schema, based on a mapping relationship between the updated first syntax elements in the updated first schema and second syntax elements in a second schema system; and combining the second syntax elements according to a context relationship between the updated first syntax elements, so as to generate a second schema
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公开(公告)号：US20250078839A1
公开(公告)日：2025-03-06
申请号：US18819018
申请日：2024-08-29
Inventor： Xiaoyin FU , Qiguang ZANG , Fenfen SHENG , Haifeng WANG , Lei JIA
IPC: G10L15/32 , G10L15/02 , G10L15/04 , G10L15/06 , G10L15/183
Abstract: A speech recognition method and a method for training a deep learning model are provided. The speech recognition method includes: obtaining a first speech feature of a speech to-be-recognized, which includes a plurality of speech segment features corresponding to a plurality of speech segments; decoding the first speech feature using a first decoder to obtain a plurality of first decoding results corresponding to a plurality of the words, indicating a first recognition result of words; extracting a second speech feature from the first speech feature based on first a priori information, which includes the plurality of first decoding results, and the second speech feature includes first word-level audio features corresponding to the plurality of words; and decoding the second speech feature using a second decoder to obtain a plurality of second decoding results corresponding to the plurality of words, indicating a second recognition result of the word.
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公开(公告)号：US20250061311A1
公开(公告)日：2025-02-20
申请号：US18746532
申请日：2024-06-18
Inventor： Zeyang LEI , Siqi BAO , Hua WU , Haifeng WANG
IPC: G06N3/0475 , G06N3/08
Abstract: A data generation method is provided. The data generation method includes: generating first answer data based on first question data from a user; determining, in response to receiving negative feedback from the user for the first answer data, a first reflection result for the first answer data based on the first answer data and the negative feedback, wherein the first reflection result indicates a diagnosis reason why feedback from the user for the first answer data is negative; and generating second answer data for the first question data based on the first question data and the first reflection result.
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公开(公告)号：US20240420684A1
公开(公告)日：2024-12-19
申请号：US18706313
申请日：2023-01-17
Inventor： Saisai ZOU , Lei JIA , Haifeng WANG
Abstract: A speech wake-up method, an electronic device, and a storage medium are provided. The method includes: performing a word recognition on a speech to be recognized to obtain a wake-up word recognition result (S210); performing a syllable recognition on the speech to be recognized to obtain a wake-up syllable recognition result, in response to determining that the wake-up word recognition result represents that the speech to be recognized contains a predetermined wake-up word (S220); and determining that the speech to be recognized is a correct wake-up speech, in response to determining that the wake-up syllable recognition result represents that the speech to be recognized contains a predetermined syllable (S230).
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15.发明公开公开(公告)号：US20240338530A1
公开(公告)日：2024-10-10
申请号：US18745550
申请日：2024-06-17
Inventor： Zhen GUO , Wenquan WU , Hua WU , Haifeng WANG
Abstract: A generative dialog model training method in the fields of artificial intelligence, such as deep learning, natural language processing, intelligent dialogs, is disclosed. The generative dialog model training method may include: in response to determination of an update of a safety specification, taking an updated safety specification as a target safety specification, and determining a dialog input corresponding to a current optimization according to the target safety specification, the update being performed on a previous safety specification when a generative dialog model after last optimization is determined not to meet a launch requirement; and optimizing the generative dialog model according to the dialog input and a principle that a reply generated by the generative dialog model conforms to the target safety specification, the generative dialog model being configured to generate the reply corresponding to the dialog input.
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Patent Agency Ranking
公开(公告)号：US20240028909A1
公开(公告)日：2024-01-25
申请号：US18478833
申请日：2023-09-29
IPC: G06N3/096
CPC classification number: G06N3/096
Abstract: A data generation method based on a deep learning model and a training method is provided. The data generation method includes: determining an initial input of the deep learning model based on input data; obtaining a first output of the model, where in response to the model determining that generating a reply based on the initial input requires calling a first functional component different from the deep learning model, the first output includes a first token for calling the first functional component and a first intermediate inquiry determined based on the initial input and recognizable by the first functional component; obtaining a first intermediate result determined by the first functional component based on the first intermediate inquiry; determining a second input for the model based on the initial input and the first intermediate result; and obtaining a second output of the model for generating a reply to the initial input.
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17.发明公开公开(公告)号：US20230360638A1
公开(公告)日：2023-11-09
申请号：US18221593
申请日：2023-07-13
Inventor： Saisai ZOU , Lei JIA , Haifeng WANG
CPC classification number: G10L15/02 , G10L15/14 , G10L2015/027
Abstract: A method of processing a speech information, a method of training a speech model, a speech wake-up method, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence technology, in particular to fields of human-computer interaction, deep learning and intelligent speech technologies. A specific implementation solution includes: performing a syllable recognition on a speech information to obtain a posterior probability sequence for the speech information, where the speech information includes a speech frame sequence, the posterior probability sequence corresponds to the speech frame sequence, and each posterior probability in the posterior probability sequence represents a similarity between a syllable in a speech frame matched with the posterior probability and a predetermined syllable; and determining a target peak speech frame from the speech frame sequence based on the posterior probability sequence.
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18.发明公开公开(公告)号：US20230317060A1
公开(公告)日：2023-10-05
申请号：US18328135
申请日：2023-06-02
Inventor： Saisai ZOU , Li CHEN , Ruoxi ZHANG , Lei JIA , Haifeng WANG
CPC classification number: G10L15/063 , G10L15/02
Abstract: The present disclosure provides a method and an apparatus for training a voice wake-up model, a method and an apparatus for voice wake-up, a device and a storage medium, which relates to the field of artificial intelligence and particularly to the field of deep learning and voice technology. A specific implementation lies in: acquiring voice recognition training data and voice wake-up training data that are created, and firstly performing training on a base model according to the voice recognition training data to obtain a model parameter of the base model when a model loss function converges; then updating, based on a model configuration instruction, a configuration parameter of a decoding module in the base model to obtain a first model; and finally performing training on the first model according to the voice wake-up training data to obtain a trained voice wake-up model when the model loss function converges.
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公开(公告)号：US20230088445A1
公开(公告)日：2023-03-23
申请号：US18059386
申请日：2022-11-28
Inventor： Zeming LIU , Hao LIU , Zhengyu Niu , Hua WU , Haifeng WANG , Hui XIONG
Abstract: A conversational recommendation method, a method of training a conversational recommendation model, an electronic device, and a storage medium are provided, which are related to a technical field of data processing, in particular to technical fields of voice interaction, deep learning, artificial intelligence and the like. The conversational recommendation method includes: acquiring a historical conversation information; determining a target conversation object to be generated, from a conversation target graph based on the historical conversation information, the conversation target graph includes an object node, the object node is configured to represent a conversation object, and the target conversation object is determined based on the object node; and generating a target conversation information for recommendation based on the target conversation object.
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20.发明申请公开(公告)号：US20250036920A1
公开(公告)日：2025-01-30
申请号：US18026140
申请日：2022-09-20
Inventor： Liang SHEN , Haifeng WANG , Huachao WU , Weibao GONG , Zhihua WU , Dianhai YU
IPC: G06N3/045 , G06N3/0495
Abstract: The present disclosure provides a mixture-of-experts (MoE) model implementation method and system, an electronic device, and a storage medium, and relates to the field of artificial intelligence (AI) such as deep learning and distributed storage. The method includes: constructing a communication group, the communication group including a tensor-parallelism communication group, the tensor-parallelism communication group including at least two computing devices, tensor-parallelism segmentation being adopted for sparse parameters of each of the computing devices in a same tensor-parallelism communication group; and training an MoE model based on the communication group. By use of the solutions of the present disclosure, normal operation of model training can be guaranteed.
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