Abstract: The present invention provides a multilingual speech recognition and translation method for a conference. The conference includes at least one attendee, and the method includes: receiving, at a server, at least one piece of audio data and at least one piece of video data generated by at least one terminal apparatus; analyzing the video data to generate a video recognition result related to an attendance, and an ethnic of the attendee and a body movement, and a facial movement of the attendee when talking; generating at least one language family recognition result according to the video recognition result and the audio data, and obtaining a plurality of audio segments corresponding to the attendee; performing speech recognition on and translating the audio segments; and displaying a translation result on the terminal apparatus. The method further determines a quantity of conference attendees according to their respective distances from their device microphones.

Abstract: A map establishment method and a map establishment system are provided. The map establishment method includes: detecting a physical motion performed by a user and generating motion sensing data by at least one motion sensor; obtaining spatial dimension information, in multiple directions, of a target place where the user is located and information of an obstacle in the target place by a deep learning model according to the motion sensing data; and generating map data according to the spatial dimension information and the information of the obstacle, wherein the map data reflects a contour of the target place where the user is located and a distribution status of at least one obstacle in the target place.

Abstract: A training method of a generator network model and an electronic device for execution thereof are provided. The training method includes: extracting a first tensor matrix and a second tensor matrix, wherein the first tensor matrix and the second tensor matrix respectively represent a first picture and a second picture and individually include a plurality of first parameters and a plurality of second parameters; generating a plurality of third pictures according to a plurality of difference values between the first parameters of the first tensor matrix and the second parameters of the second tensor matrix; performing a similarity test on a plurality of original pictures and the plurality of third pictures; and adopting at least one of the third pictures whose similarity is lower than or equal to a similarity threshold as at least one new sample picture.

Abstract: A method for correcting infant crying identification includes the following steps: a detecting step provides an audio unit to detect a sound around an infant to generate a plurality of audio samples. A converting step provides a processing unit to convert the audio samples to generate a plurality of audio spectrograms. An extracting step provides a common model to extract the audio spectrograms to generate a plurality of infant crying features. An incremental training step provides an incremental model to train the infant crying features to generate an identification result. A judging step provides the processing unit to judge whether the identification result is correct according to a real result of the infant. When the identification result is different from the real result, an incorrect result is generated. A correcting step provides the processing unit to correct the incremental model according to the incorrect result.

Abstract: The present invention provides a multilingual speech recognition and translation method for a conference. The conference includes at least one attendee, and the method includes: receiving, at a server , at least one piece of audio data and at least one piece of video data generated by at least one terminal apparatus; analyzing the video data to generate a video recognition result related to an attendance, and an ethnic of the attendee and a body movement, and a facial movement of the attendee when talking; generating at least one language family recognition result according to the video recognition result and the audio data, and obtaining a plurality of audio segments corresponding to the attendee; performing speech recognition on and translating the audio segments; and displaying a translation result on the terminal apparatus.

Abstract: A method for correcting infant crying identification includes the following steps: a detecting step provides an audio unit to detect a sound around an infant to generate a plurality of audio samples. A converting step provides a processing unit to convert the audio samples to generate a plurality of audio spectrograms. An extracting step provides a common model to extract the audio spectrograms to generate a plurality of infant crying features. An incremental training step provides an incremental model to train the infant crying features to generate an identification result. A judging step provides the processing unit to judge whether the identification result is correct according to a real result of the infant. When the identification result is different from the real result, an incorrect result is generated. A correcting step provides the processing unit to correct the incremental model according to the incorrect result.

Abstract: A second-harmonic generation nonlinear frequency converter includes a nonlinear optical crystal. The nonlinear optical crystal includes a plurality of sections. The sections connect to each other in sequence, and each section has a phase different from others. Each of the phases includes a positive domain and a negative domain. Each of the sections includes a plurality of quasi-phase-matching structures. The quasi-phase-matching structures connect to each other in sequence and have the same phase in one section.

Abstract: A second-harmonic generation nonlinear frequency converter includes a nonlinear optical crystal. The nonlinear optical crystal includes a plurality of sections. The sections connect to each other in sequence, and each section has a phase different from others. Each of the phases includes a positive domain and a negative domain. Each of the sections includes a plurality of quasi-phase-matching structures. The quasi-phase-matching structures connect to each other in sequence and have the same phase in one section.