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기록

Voice Activity Detection Method, Electronic Device, and Non-Transitory Readable Storage Medium

발명심사 중
12조회수
20청구항 · 3 독립항
§ Ⅰ

개요

IPC 분류

G10L 25/78G6N 3/464G10L 25/30

CPC 분류

G10L25/78G6N3/464G10L25/30G10L2025/783

A voice activity detection method includes; obtaining a target audio feature of a target audio signal, inputting the target audio feature into a first network layer of a target model to obtain a first feature map including N first channels, inputting the first feature map into a second network layer of the target model to obtain a second feature map including N second channels, and outputting a voice activity detection category based on the second feature map. Each first channel includes one target feature matrix, and each target feature matrix is obtained by the first network layer by performing high-level feature extraction on the target audio feature. Each second channel corresponds to one first channel, each second channel includes one target feature value, and each target feature value is obtained by the second network layer by performing temporal modeling on a corresponding target feature matrix.

원문 (중국어)

A voice activity detection method includes; obtaining a target audio feature of a target audio signal, inputting the target audio feature into a first network layer of a target model to obtain a first feature map including N first channels, inputting the first feature map into a second network layer of the target model to obtain a second feature map including N second channels, and outputting a voice activity detection category based on the second feature map. Each first channel includes one target feature matrix, and each target feature matrix is obtained by the first network layer by performing high-level feature extraction on the target audio feature. Each second channel corresponds to one first channel, each second channel includes one target feature value, and each target feature value is obtained by the second network layer by performing temporal modeling on a corresponding target feature matrix.