LIGHT INTERFERENCE GENERATOR AND INTERFERENCE IMAGING DEVICE

    公开(公告)号:US20220349755A1

    公开(公告)日:2022-11-03

    申请号:US17760983

    申请日:2020-09-30

    发明人: Tatsuki TAHARA

    IPC分类号: G01J3/45 G01J3/433

    摘要: An interference imaging device includes a light interference generator that includes: a light wave splitter configured to reflect a part of incident light and to allow a remaining part of the incident light to pass through; a phase modulator configured to modulate a phase of incident light that has passed through the light wave splitter; and a reflector configured to reflect the phase-modulated incident light from the phase modulator so that the reflected, phase-modulated incident light overlaps with incident light that has been reflected by the light wave splitter.

    ATOMIC RESONATOR
    4.
    发明申请

    公开(公告)号:US20220200612A1

    公开(公告)日:2022-06-23

    申请号:US17692742

    申请日:2022-03-11

    IPC分类号: H03L7/26 G04F5/14

    摘要: This atomic resonator for causing a resonance frequency by CPT resonance includes: a gas cell having alkali metal atoms enclosed; a photodetector configured to detect light having passed through the gas cell and convert the light to an electric signal; a high-frequency oscillator configured to receive the electric signal and output the signal after a frequency thereof is divided by two; and a laser light source configured to modulate and introduce, into the gas cell, light based on the signal output from the high-frequency oscillator. The high-frequency oscillator has an injection-locked frequency divider circuit including an acoustic resonator as an oscillation element.

    Spoken dialog device, spoken dialog method, and recording medium

    公开(公告)号:US11049493B2

    公开(公告)日:2021-06-29

    申请号:US16320810

    申请日:2017-07-24

    摘要: [Problem] With conventional technology, it is impossible to appropriately support spoken dialog that is carried out in multiple languages. [Solution] A spoken dialog device includes: a receiving unit that detects a voice section from a start point to an end point of an input speech that is spoken in any of two or more different languages, and acquires speech data corresponding to the voice section; a language identifier acquisition unit that acquires a language identifier that identifies a language in which the input speech was spoken; a speech recognition unit that generates a text resulting from speech recognition, based on the input speech and the language identifier; a dialog control unit to which a text resulting from speech recognition and a language identifier are input, and that generates a different output sentence depending on a language identifier, while maintaining dialog history even when the language identifier is different from the previous language identifier; a speech synthesizing unit that generates a speech waveform based on the output sentence and the language identifier; and a speech output unit that outputs a speech that is based on a speech waveform generated by the speech synthesizing unit. With such a spoken dialog device, it is possible to appropriately support spoken dialog that is carried out in multiple languages.

    PSEUDO PARALLEL TRANSLATION DATA GENERATION APPARATUS, MACHINE TRANSLATION PROCESSING APPARATUS, AND PSEUDO PARALLEL TRANSLATION DATA GENERATION METHOD

    公开(公告)号:US20210027026A1

    公开(公告)日:2021-01-28

    申请号:US16969619

    申请日:2019-02-12

    IPC分类号: G06F40/58 G06N3/08 G06K9/62

    摘要: Provided are a model training method for neural machine translation that enhances an encoder using a monolingual corpus of a target language and improves the accuracy of the entire translator, and a machine translation system for performing the model training method. The machine translation system 1000 uses a monolingual corpus of the target language to obtain multiple pieces of pseudo source language data, thus allowing for obtaining a large amount of pseudo parallel corpus data having diversity. Further, the machine translation system 1000 uses both the pseudo parallel corpus data having diversity, which has been obtained in large quantities, and the base parallel corpus data in a small quantity but with high accuracy, with the applied learning rates changed accordingly, to perform the learning process (training process) for the machine translation model. This allows the machine translation system 1000 to obtain a learned model (machine translation model) with very high accuracy.