Spoofing speech detection algorithm based on joint feature and random forest
In order to describe the characteristic information of the speech signal more comprehensively and improve the detection rate of camouflage, a spoofing speech detection method based on the combination of uniform local binary pattern texture feature and constant Q cepstrum coefficient acoustic feature...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2022-06-01
|
| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022089/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850031303108329472 |
|---|---|
| author | Jiaqi YU Zhihua JIAN Jia XU Lin YOU Yunlu WANG Chao WU |
| author_facet | Jiaqi YU Zhihua JIAN Jia XU Lin YOU Yunlu WANG Chao WU |
| author_sort | Jiaqi YU |
| collection | DOAJ |
| description | In order to describe the characteristic information of the speech signal more comprehensively and improve the detection rate of camouflage, a spoofing speech detection method based on the combination of uniform local binary pattern texture feature and constant Q cepstrum coefficient acoustic feature was proposed, which used random forest as the classifier model.The texture feature vector in the speech signal spectrogram was extracted by using the uniform local binary mode, and the joint feature was formed with the constant Q cepstrum coefficient.Then, the obtained joint feature vector was used to train the random forest classifier, so as to realize the camouflage speech detection.In the experiment, the performances of several spoofing detection systems constructed by other feature parameters and the support vector machine classifier model were compared, and the results show that the proposed speech spoofing detection system combined with the joint feature and the random forest model has the best performance. |
| format | Article |
| id | doaj-art-dc43b120b5d14a5c8122663668e1c535 |
| institution | DOAJ |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2022-06-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-dc43b120b5d14a5c8122663668e1c5352025-08-20T02:59:00ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012022-06-0138919959811292Spoofing speech detection algorithm based on joint feature and random forestJiaqi YUZhihua JIANJia XULin YOUYunlu WANGChao WUIn order to describe the characteristic information of the speech signal more comprehensively and improve the detection rate of camouflage, a spoofing speech detection method based on the combination of uniform local binary pattern texture feature and constant Q cepstrum coefficient acoustic feature was proposed, which used random forest as the classifier model.The texture feature vector in the speech signal spectrogram was extracted by using the uniform local binary mode, and the joint feature was formed with the constant Q cepstrum coefficient.Then, the obtained joint feature vector was used to train the random forest classifier, so as to realize the camouflage speech detection.In the experiment, the performances of several spoofing detection systems constructed by other feature parameters and the support vector machine classifier model were compared, and the results show that the proposed speech spoofing detection system combined with the joint feature and the random forest model has the best performance.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022089/spoofing speech detectionacoustic featuretexture featureuniform local binary patternrandom forest |
| spellingShingle | Jiaqi YU Zhihua JIAN Jia XU Lin YOU Yunlu WANG Chao WU Spoofing speech detection algorithm based on joint feature and random forest Dianxin kexue spoofing speech detection acoustic feature texture feature uniform local binary pattern random forest |
| title | Spoofing speech detection algorithm based on joint feature and random forest |
| title_full | Spoofing speech detection algorithm based on joint feature and random forest |
| title_fullStr | Spoofing speech detection algorithm based on joint feature and random forest |
| title_full_unstemmed | Spoofing speech detection algorithm based on joint feature and random forest |
| title_short | Spoofing speech detection algorithm based on joint feature and random forest |
| title_sort | spoofing speech detection algorithm based on joint feature and random forest |
| topic | spoofing speech detection acoustic feature texture feature uniform local binary pattern random forest |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022089/ |
| work_keys_str_mv | AT jiaqiyu spoofingspeechdetectionalgorithmbasedonjointfeatureandrandomforest AT zhihuajian spoofingspeechdetectionalgorithmbasedonjointfeatureandrandomforest AT jiaxu spoofingspeechdetectionalgorithmbasedonjointfeatureandrandomforest AT linyou spoofingspeechdetectionalgorithmbasedonjointfeatureandrandomforest AT yunluwang spoofingspeechdetectionalgorithmbasedonjointfeatureandrandomforest AT chaowu spoofingspeechdetectionalgorithmbasedonjointfeatureandrandomforest |