公开(公告)号：US20210074270A1

公开(公告)日：2021-03-11

申请号：US17013391

申请日：2020-09-04

Applicant: HYPERCONNECT, INC.

Inventor： Sang Il Ahn ,  Seung Woo Choi ,  Seok Jun Seo ,  Beom Jun Shin

IPC: G10L15/16 ,  G10L25/24

Abstract: A keyword spotting apparatus, method, and computer-readable recording medium are disclosed. The keyword spotting method using an artificial neural network according to an embodiment of the disclosure may include obtaining an input feature map from an input voice; performing a first convolution operation on the input feature map for each of n different filters having the same channel length as the input feature map, wherein a width of each of the filters is w1 and the width w1 is less than a width of the input feature map; performing a second convolution operation on a result of the first convolution operation for each of different filters having the same channel length as the input feature map; storing a result of the second convolution operation as an output feature map; and extracting a voice keyword by applying the output feature map to a learned machine learning model.

公开(公告)号：US20230162724A9

公开(公告)日：2023-05-25

申请号：US17013391

申请日：2020-09-04

Applicant: HYPERCONNECT, INC.

Inventor： Sang Il Ahn ,  Seung Woo Choi ,  Seok Jun Seo ,  Beom Jun Shin

IPC: G10L15/16 ,  G10L25/24

CPC classification number: G10L15/16 ,  G10L25/24 ,  G10L2015/088

Abstract: A keyword spotting apparatus, method, and computer-readable recording medium are disclosed. The keyword spotting method using an artificial neural network according to an embodiment of the disclosure may include obtaining an input feature map from an input voice; performing a first convolution operation on the input feature map for each of n different filters having the same channel length as the input feature map, wherein a width of each of the filters is w1 and the width w1 is less than a width of the input feature map; performing a second convolution operation on a result of the first convolution operation for each of different filters having the same channel length as the input feature map; storing a result of the second convolution operation as an output feature map; and extracting a voice keyword by applying the output feature map to a learned machine learning model.

公开(公告)号：US20220199068A1

公开(公告)日：2022-06-23

申请号：US17455211

申请日：2021-11-16

Applicant: Hyperconnect, Inc.

Inventor： Sang Il Ahn ,  Seung Woo Choi ,  Seung Ju Han ,  Dong Young Kim ,  Sung Joo Ha

IPC: G10L13/047 ,  G10L15/16 ,  G06N3/04

Abstract: Disclosed is a speech synthesis method including acquiring second speech data and a target text, acquiring first information includes embedding information corresponding to the second speech data, acquiring second information including embedding information of the second speech data, the embedding information in relation with components generated based on a sequence of the target text, and acquiring audio data corresponding to the target text and reflecting characteristics of speech of a speaker based on the first information and the second information.

公开(公告)号：US11854536B2

公开(公告)日：2023-12-26

申请号：US17013391

申请日：2020-09-04

Applicant: Hyperconnect Inc.

Inventor： Sang Il Ahn ,  Seung Woo Choi ,  Seok Jun Seo ,  Beom Jun Shin

IPC: G10L15/16 ,  G10L25/24 ,  G10L15/08

CPC classification number: G10L15/16 ,  G10L25/24 ,  G10L2015/088

Abstract: A keyword spotting apparatus, method, and computer-readable recording medium are disclosed. The keyword spotting method using an artificial neural network according to an embodiment of the disclosure may include obtaining an input feature map from an input voice; performing a first convolution operation on the input feature map for each of n different filters having the same channel length as the input feature map, wherein a width of each of the filters is w1 and the width w1 is less than a width of the input feature map; performing a second convolution operation on a result of the first convolution operation for each of different filters having the same channel length as the input feature map; storing a result of the second convolution operation as an output feature map; and extracting a voice keyword by applying the output feature map to a learned machine learning model.