Speaker verification method based on deep neural network, terminal and storage medium
The invention discloses a speaker verification method based on a deep neural network, a terminal and a storage medium. The method comprises the following steps: acquiring voice data of a plurality of speakers in a preset data set; converting the plurality of voice data into a two-dimensional data gr...
Gespeichert in:
Hauptverfasser: | , , |
---|---|
Format: | Patent |
Sprache: | chi ; eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | YANG BO LIANG XINGWEI ZHUANG XINNAN |
description | The invention discloses a speaker verification method based on a deep neural network, a terminal and a storage medium. The method comprises the following steps: acquiring voice data of a plurality of speakers in a preset data set; converting the plurality of voice data into a two-dimensional data group through preprocessing, and dividing the two-dimensional data group into a training set and a verification set according to a preset proportion; constructing a deep neural network according to the residual neural network and the long-short term memory network, and performing training verification on the deep neural network through the training set and the verification set to obtain a trained deep neural network; and through the trained deep neural network, predicting a plurality of pieces of input audio information of the to-be-tested speaker, and outputting a verification result of the to-be-tested speaker. The method makes full use of the frequency domain feature and time domain feature information of the audi |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN115223569A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN115223569A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN115223569A3</originalsourceid><addsrcrecordid>eNqNjDsOwjAQRN1QIOAOSw9FEgWJEkUgKhqgjpZ4Albij9YOXB8XHIDqaUZvZq7u1wAeIPSGmN50nIx3ZJFeXtODIzTlrIFADpPwmJE-XoYNJYg1LjfsNMXkhZ_IS20mu1SznseI1Y8LtT4db815i-BbxMAd8k3bXIqiLsuq3u0P1T_OF9xbOSs</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Speaker verification method based on deep neural network, terminal and storage medium</title><source>esp@cenet</source><creator>YANG BO ; LIANG XINGWEI ; ZHUANG XINNAN</creator><creatorcontrib>YANG BO ; LIANG XINGWEI ; ZHUANG XINNAN</creatorcontrib><description>The invention discloses a speaker verification method based on a deep neural network, a terminal and a storage medium. The method comprises the following steps: acquiring voice data of a plurality of speakers in a preset data set; converting the plurality of voice data into a two-dimensional data group through preprocessing, and dividing the two-dimensional data group into a training set and a verification set according to a preset proportion; constructing a deep neural network according to the residual neural network and the long-short term memory network, and performing training verification on the deep neural network through the training set and the verification set to obtain a trained deep neural network; and through the trained deep neural network, predicting a plurality of pieces of input audio information of the to-be-tested speaker, and outputting a verification result of the to-be-tested speaker. The method makes full use of the frequency domain feature and time domain feature information of the audi</description><language>chi ; eng</language><subject>ACOUSTICS ; MUSICAL INSTRUMENTS ; PHYSICS ; SPEECH ANALYSIS OR SYNTHESIS ; SPEECH OR AUDIO CODING OR DECODING ; SPEECH OR VOICE PROCESSING ; SPEECH RECOGNITION</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20221021&DB=EPODOC&CC=CN&NR=115223569A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20221021&DB=EPODOC&CC=CN&NR=115223569A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>YANG BO</creatorcontrib><creatorcontrib>LIANG XINGWEI</creatorcontrib><creatorcontrib>ZHUANG XINNAN</creatorcontrib><title>Speaker verification method based on deep neural network, terminal and storage medium</title><description>The invention discloses a speaker verification method based on a deep neural network, a terminal and a storage medium. The method comprises the following steps: acquiring voice data of a plurality of speakers in a preset data set; converting the plurality of voice data into a two-dimensional data group through preprocessing, and dividing the two-dimensional data group into a training set and a verification set according to a preset proportion; constructing a deep neural network according to the residual neural network and the long-short term memory network, and performing training verification on the deep neural network through the training set and the verification set to obtain a trained deep neural network; and through the trained deep neural network, predicting a plurality of pieces of input audio information of the to-be-tested speaker, and outputting a verification result of the to-be-tested speaker. The method makes full use of the frequency domain feature and time domain feature information of the audi</description><subject>ACOUSTICS</subject><subject>MUSICAL INSTRUMENTS</subject><subject>PHYSICS</subject><subject>SPEECH ANALYSIS OR SYNTHESIS</subject><subject>SPEECH OR AUDIO CODING OR DECODING</subject><subject>SPEECH OR VOICE PROCESSING</subject><subject>SPEECH RECOGNITION</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjDsOwjAQRN1QIOAOSw9FEgWJEkUgKhqgjpZ4Albij9YOXB8XHIDqaUZvZq7u1wAeIPSGmN50nIx3ZJFeXtODIzTlrIFADpPwmJE-XoYNJYg1LjfsNMXkhZ_IS20mu1SznseI1Y8LtT4db815i-BbxMAd8k3bXIqiLsuq3u0P1T_OF9xbOSs</recordid><startdate>20221021</startdate><enddate>20221021</enddate><creator>YANG BO</creator><creator>LIANG XINGWEI</creator><creator>ZHUANG XINNAN</creator><scope>EVB</scope></search><sort><creationdate>20221021</creationdate><title>Speaker verification method based on deep neural network, terminal and storage medium</title><author>YANG BO ; LIANG XINGWEI ; ZHUANG XINNAN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN115223569A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</creationdate><topic>ACOUSTICS</topic><topic>MUSICAL INSTRUMENTS</topic><topic>PHYSICS</topic><topic>SPEECH ANALYSIS OR SYNTHESIS</topic><topic>SPEECH OR AUDIO CODING OR DECODING</topic><topic>SPEECH OR VOICE PROCESSING</topic><topic>SPEECH RECOGNITION</topic><toplevel>online_resources</toplevel><creatorcontrib>YANG BO</creatorcontrib><creatorcontrib>LIANG XINGWEI</creatorcontrib><creatorcontrib>ZHUANG XINNAN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>YANG BO</au><au>LIANG XINGWEI</au><au>ZHUANG XINNAN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Speaker verification method based on deep neural network, terminal and storage medium</title><date>2022-10-21</date><risdate>2022</risdate><abstract>The invention discloses a speaker verification method based on a deep neural network, a terminal and a storage medium. The method comprises the following steps: acquiring voice data of a plurality of speakers in a preset data set; converting the plurality of voice data into a two-dimensional data group through preprocessing, and dividing the two-dimensional data group into a training set and a verification set according to a preset proportion; constructing a deep neural network according to the residual neural network and the long-short term memory network, and performing training verification on the deep neural network through the training set and the verification set to obtain a trained deep neural network; and through the trained deep neural network, predicting a plurality of pieces of input audio information of the to-be-tested speaker, and outputting a verification result of the to-be-tested speaker. The method makes full use of the frequency domain feature and time domain feature information of the audi</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | chi ; eng |
recordid | cdi_epo_espacenet_CN115223569A |
source | esp@cenet |
subjects | ACOUSTICS MUSICAL INSTRUMENTS PHYSICS SPEECH ANALYSIS OR SYNTHESIS SPEECH OR AUDIO CODING OR DECODING SPEECH OR VOICE PROCESSING SPEECH RECOGNITION |
title | Speaker verification method based on deep neural network, terminal and storage medium |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T18%3A27%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=YANG%20BO&rft.date=2022-10-21&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN115223569A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |