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    Abstract generation device, method, program, and recording medium
    2.
    发明授权
    Abstract generation device, method, program, and recording medium 有权

    公开(公告)号：US11869491B2

    公开(公告)日：2024-01-09

    申请号：US17425696

    申请日：2020-01-16

    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

    Inventor： Tsutomu Hirao ,  Atsunori Ogawa ,  Tomohiro Nakatani ,  Masaaki Nagata

    IPC: G10L15/193 ,  G10L15/08 ,  G10L15/22

    CPC classification number: G10L15/193 ,  G10L15/083 ,  G10L15/22

    Abstract: A speech recognition unit converts an input utterance sequence into a confusion network sequence constituted by a k-best of candidate words of speech recognition results; a lattice generating unit generates a lattice sequence having the candidate words as internal nodes and a combination of k words among the candidate words for an identical speech as an external node, in which edges are extended between internal nodes other than internal nodes included in an identical external node, from the confusion network sequence; an integer programming problem generating unit generates an integer programming problem for selecting a path that maximizes an objective function including at least a coverage score of an important word, of paths following the internal nodes with the edges extended, in the lattice sequence; and the summary generating unit generates a high-quality summary having less speech recognition errors and low redundancy using candidate words indicated by the internal nodes included in the path selected by solving the integer programming problem, under a constraint on the length of a summary to be generated.

    ALIAS-BASED ACCESS OF ENTITY INFORMATION OVER VOICE-ENABLED DIGITAL ASSISTANTS
    4.
    发明公开
    ALIAS-BASED ACCESS OF ENTITY INFORMATION OVER VOICE-ENABLED DIGITAL ASSISTANTS 审中-公开

    公开(公告)号：US20230154458A1

    公开(公告)日：2023-05-18

    申请号：US18098206

    申请日：2023-01-18

    Applicant: VeriSign, Inc.

    Inventor： Andrew FREGLY ,  Burton S. KALISKI, JR. ,  Swapneel SHETH

    IPC: G10L15/18 ,  G06F3/16 ,  G10L15/193 ,  G06F16/955

    CPC classification number: G10L15/1822 ,  G06F3/167 ,  G10L15/193 ,  G06F16/955

    Abstract: In one embodiment, a domain-name based framework implemented in a digital assistant ecosystem uses domain names as unique identifiers for request types, requesting entities, responders, and target entities embedded in a natural language request. Further, the framework enables interpreting natural language requests according to domain ontologies associated with different responders. A domain ontology operates as a keyword dictionary for a given responder and defines the keywords and corresponding allowable values to be used for request types and request parameters. The domain-name based framework thus enables the digital assistant to interact with any responder that supports a domain ontology to generate precise and complete responses to natural language based requests.

    Automatic Learning of Language Models
    5.
    发明申请
    Automatic Learning of Language Models 审中-公开

    公开(公告)号：US20180293977A1

    公开(公告)日：2018-10-11

    申请号：US15483977

    申请日：2017-04-10

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Christian Liensberger

    IPC: G10L15/187 ,  G06F17/27 ,  G10L15/06 ,  G10L15/18 ,  G10L15/22

    CPC classification number: G10L15/22 ,  G06F17/2775 ,  G10L15/183 ,  G10L15/193 ,  G10L2015/228

    Abstract: Techniques and systems are disclosed for context-dependent speech recognition. The techniques and systems described enable accurate recognition of speech by accessing sub-libraries associated with the context of the speech to be recognized. These techniques translate audible input into audio data at a smart device and determine context for the speech, such as location-based, temporal-based, recipient-based, and application based context. The smart device then accesses a context-dependent library to compare the audio data with phrase-associated translation data in one or more sub-libraries of the context-dependent library to determine a match. In this way, the techniques allow access to a large quantity of phrases while reducing incorrect matching of the audio data to translation data caused by organizing the phrases into context-dependent sub-libraries.

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