Method for detecting keyword in speech signal, terminal, and storage medium

    公开(公告)号:US11341957B2

    公开(公告)日:2022-05-24

    申请号:US16933446

    申请日:2020-07-20

    Abstract: A method for detecting a keyword, applied to a terminal, includes: extracting a speech eigenvector of a speech signal; obtaining, according to the speech eigenvector, a posterior probability of each target character being a key character in any keyword in an acquisition time period of the speech signal; obtaining confidences of at least two target character combinations according to the posterior probability of each target character; and determining that the speech signal includes the keyword upon determining that all the confidences of the at least two target character combinations meet a preset condition. The target character is a character in the speech signal whose pronunciation matches a pronunciation of the key character. Each target character combination includes at least one target character, and a confidence of a target character combination represents a probability of the target character combination being the keyword or a part of the keyword.

    Bad disk block self-detection method and apparatus, and computer storage medium
    3.
    发明申请
    Bad disk block self-detection method and apparatus, and computer storage medium 审中-公开
    坏磁盘块自检方法和装置,以及计算机存储介质

    公开(公告)号:US20140372838A1

    公开(公告)日:2014-12-18

    申请号:US14368453

    申请日:2013-04-25

    CPC classification number: G06F11/1076 G06F3/0619 G06F3/0647 G06F3/0689

    Abstract: It is described a bad disk block self-detection method, including: each mounted chunk is partitioned into n sub-chunks, all sub-chunks having a same size, where n is an integer not less than 2; checking information is set at a fixed location of each sub-chunk, data is stored onto locations of each sub-chunk other than the fixed location, where the checking information is parity checking information for the data; and when the data is read or written, data verification is performed based on the checking information set at the fixed location of the read sub-chunk. It is also described a bad disk block self-detection apparatus and a computer storage medium. With the described above, the bad block on the disk can be detected rapidly, and it is able to instruct data migration and disk replacement.

    Abstract translation: 描述了一种不好的磁盘块自检方法,包括:每个安装的块被划分为n个子块,所有子块具有相同的大小,其中n是不小于2的整数; 检查信息被设置在每个子块的固定位置,数据被存储到除了固定位置之外的每个子块的位置,其中检查信息是数据的奇偶校验信息; 并且当读取或写入数据时,基于在读取的子块的固定位置处设置的检查信息执行数据验证。 还描述了坏盘自检装置和计算机存储介质。 如上所述,可以快速检测磁盘上的坏块,并能指示数据迁移和磁盘更换。

    Biological feature recognition method and apparatus, and storage medium

    公开(公告)号:US10824848B2

    公开(公告)日:2020-11-03

    申请号:US16201701

    申请日:2018-11-27

    Inventor: Jie Chen

    Abstract: The present disclosure discloses a biological feature recognition method performed at a biological feature recognition apparatus. After obtaining a facial image of a user and current heart rate data of the user, the apparatus determines, according to the facial image, current facial color data of the user and a correspondence between facial color data and heart rate data of the user and then determines, according to the correspondence between the facial color data and the heart rate data of the user, whether the current facial color data matches the current heart rate data. If the current facial color data matches the current heart rate data, the apparatus determines that recognition succeeds, thereby effectively avoiding a loophole of biological recognition in application.

    Content distribution method, system and server

    公开(公告)号:US10334023B2

    公开(公告)日:2019-06-25

    申请号:US14449293

    申请日:2014-08-01

    Abstract: The present invention discloses a content distribution method, system and a server. In one embodiment, the method includes: receiving a content distribution request form a client; obtaining all receiving ends designated by the content distribution request, and marking at least a portion of the receiving ends with a first status code; judging whether all the at least a portion of the receiving ends complete the distribution task, if not, controlling an internal distribution process until all the at least a portion of the receiving ends complete the distribution task.

    METHOD FOR RECOVERING HARD DISK DATA, SERVER AND DISTRIBUTED STORAGE SYSTEM
    6.
    发明申请
    METHOD FOR RECOVERING HARD DISK DATA, SERVER AND DISTRIBUTED STORAGE SYSTEM 审中-公开
    用于恢复硬盘数据,服务器和分布式存储系统的方法

    公开(公告)号:US20140365824A1

    公开(公告)日:2014-12-11

    申请号:US14372662

    申请日:2013-01-05

    Abstract: A method for recovering hard disk data, a server and a distributed storage system relate to a computer technology. In the method, a data recovery request is received. The request includes at least one ID of sectors whose data is to be recovered. Based on the at least one ID of the sectors whose data is to be recovered, at least one sector whose data is to be recovered is located. Obtain at least one standby sector ID and a file backup corresponding to the at least one ID of the sectors whose data is to be recovered, and locate at least one standby sector based on the at least one standby sector ID. Write, into the at least one standby sector, data that is in the file backup and the same as the data stored in the at least one sector whose data is to be recovered.

    Abstract translation: 用于恢复硬盘数据的方法,服务器和分布式存储系统涉及计算机技术。 在该方法中,接收到数据恢复请求。 请求包括要恢复其数据的扇区的至少一个ID。 基于要恢复其数据的扇区的至少一个ID,要存储要恢复其数据的至少一个扇区。 获取至少一个备用扇区ID和对应于其数据要恢复的扇区的至少一个ID的文件备份,并且基于该至少一个备用扇区ID来定位至少一个备用扇区。 将至少一个备用扇区写入文件备份中的数据与存储在数据要被恢复的至少一个扇区中的数据相同。

    ARTIFICIAL INTELLIGENCE-BASED WAKEUP WORD DETECTION METHOD AND APPARATUS, DEVICE, AND MEDIUM

    公开(公告)号:US20220013111A1

    公开(公告)日:2022-01-13

    申请号:US17483617

    申请日:2021-09-23

    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.

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