Publicação
Speaker recognition for door opening systems
| Resumo: | Besides being an important communication tool, the voice can also serve for identification purposes since it has an individual signature for each person. Speaker recognition technologies can use this signature as an authentication method to access environments. This work explores the development and testing of machine and deep learning models, specifically the GMM, the VGG-M, and ResNet50 models, for speaker recognition access control to build a system to grant access to CeDRI’s laboratory. The deep learning models were evaluated based on their performance in recognizing speakers from audio samples, emphasizing the Equal Error Rate metric to determine their effectiveness. The models were trained and tested initially in public datasets with 1251 to 6112 speakers and then fine-tuned on private datasets with 32 speakers of CeDri’s laboratory. In this study, we compared the performance of ResNet50, VGGM, and GMM models for speaker verification. After conducting experiments on our private datasets, we found that the ResNet50 model outperformed the other models. It achieved the lowest Equal Error Rate (EER) of 0.7% on the Framed Silence Removed dataset. On the same dataset,« the VGGM model achieved an EER of 5%, and the GMM model achieved an EER of 2.13%. Our best model’s performance was unable to achieve the current state-of-the-art of 2.87% in the VoxCeleb 1 verification dataset. However, our best implementation using ResNet50 achieved an EER of 5.96% while being trained on only a tiny portion of the data than it usually is. So, this result indicates that our model is robust and efficient and provides a significant improvement margin. This thesis provides insights into the capabilities of these models in a real-world application, aiming to deploy the system on a platform for practical use in laboratory access authorization. The results of this study contribute to the field of biometric security by demonstrating the potential of speaker recognition systems in controlled environments. |
|---|---|
| Autores principais: | Manfron, Enrico |
| Assunto: | Besides Communication tool Deep learning model |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867173246291410944 |
|---|---|
| author | Manfron, Enrico |
| author_facet | Manfron, Enrico |
| author_role | author |
| contributor_name_str_mv | Teixeira, João Paulo Minetto, Rodrigo Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Manfron, Enrico\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Teixeira, João Paulo Minetto, Rodrigo Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Manfron, Enrico |
| datacite.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-01-03T14:47:47Z |
| datacite.date.embargoed.fl_str_mv | 2024-01-03T14:47:47Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Besides Communication tool Deep learning model |
| datacite.titles.title.fl_str_mv | Speaker recognition for door opening systems |
| dc.contributor.none.fl_str_mv | Teixeira, João Paulo Minetto, Rodrigo Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Manfron, Enrico |
| dc.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-01-03T14:47:47Z |
| dc.date.embargoed.fl_str_mv | 2024-01-03T14:47:47Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/29072 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by-nc/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Besides Communication tool Deep learning model |
| dc.title.fl_str_mv | Speaker recognition for door opening systems |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | Besides being an important communication tool, the voice can also serve for identification purposes since it has an individual signature for each person. Speaker recognition technologies can use this signature as an authentication method to access environments. This work explores the development and testing of machine and deep learning models, specifically the GMM, the VGG-M, and ResNet50 models, for speaker recognition access control to build a system to grant access to CeDRI’s laboratory. The deep learning models were evaluated based on their performance in recognizing speakers from audio samples, emphasizing the Equal Error Rate metric to determine their effectiveness. The models were trained and tested initially in public datasets with 1251 to 6112 speakers and then fine-tuned on private datasets with 32 speakers of CeDri’s laboratory. In this study, we compared the performance of ResNet50, VGGM, and GMM models for speaker verification. After conducting experiments on our private datasets, we found that the ResNet50 model outperformed the other models. It achieved the lowest Equal Error Rate (EER) of 0.7% on the Framed Silence Removed dataset. On the same dataset,« the VGGM model achieved an EER of 5%, and the GMM model achieved an EER of 2.13%. Our best model’s performance was unable to achieve the current state-of-the-art of 2.87% in the VoxCeleb 1 verification dataset. However, our best implementation using ResNet50 achieved an EER of 5.96% while being trained on only a tiny portion of the data than it usually is. So, this result indicates that our model is robust and efficient and provides a significant improvement margin. This thesis provides insights into the capabilities of these models in a real-world application, aiming to deploy the system on a platform for practical use in laboratory access authorization. The results of this study contribute to the field of biometric security by demonstrating the potential of speaker recognition systems in controlled environments. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/4d200ef7-8b4a-41ca-87ee-371afd682c58/download |
| id | ipb_3f80efc43bf9bf85ebee2fd317bceee8 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/29072 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/29072 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Manfron, Enrico |
| publishDate | 2023 |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engpt_PTBesides being an important communication tool, the voice can also serve for identification purposes since it has an individual signature for each person. Speaker recognition technologies can use this signature as an authentication method to access environments. This work explores the development and testing of machine and deep learning models, specifically the GMM, the VGG-M, and ResNet50 models, for speaker recognition access control to build a system to grant access to CeDRI’s laboratory. The deep learning models were evaluated based on their performance in recognizing speakers from audio samples, emphasizing the Equal Error Rate metric to determine their effectiveness. The models were trained and tested initially in public datasets with 1251 to 6112 speakers and then fine-tuned on private datasets with 32 speakers of CeDri’s laboratory. In this study, we compared the performance of ResNet50, VGGM, and GMM models for speaker verification. After conducting experiments on our private datasets, we found that the ResNet50 model outperformed the other models. It achieved the lowest Equal Error Rate (EER) of 0.7% on the Framed Silence Removed dataset. On the same dataset,« the VGGM model achieved an EER of 5%, and the GMM model achieved an EER of 2.13%. Our best model’s performance was unable to achieve the current state-of-the-art of 2.87% in the VoxCeleb 1 verification dataset. However, our best implementation using ResNet50 achieved an EER of 5.96% while being trained on only a tiny portion of the data than it usually is. So, this result indicates that our model is robust and efficient and provides a significant improvement margin. This thesis provides insights into the capabilities of these models in a real-world application, aiming to deploy the system on a platform for practical use in laboratory access authorization. The results of this study contribute to the field of biometric security by demonstrating the potential of speaker recognition systems in controlled environments.application/pdfpt_PTSpeaker recognition for door opening systemsManfron, EnricoTeixeira, João PauloMinetto, RodrigoHostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptURNurn:tid:2034445822024-01-03T14:47:47Z20232023-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/29072http://purl.org/coar/access_right/c_abf2open accessBesidesCommunication toolDeep learning model3430411 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesis2023http://creativecommons.org/licenses/by-nc/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/4d200ef7-8b4a-41ca-87ee-371afd682c58/download |
| spellingShingle | Speaker recognition for door opening systems Manfron, Enrico Besides Communication tool Deep learning model |
| status | SINGLETON |
| subject.fl_str_mv | Besides Communication tool Deep learning model |
| title | Speaker recognition for door opening systems |
| title_full | Speaker recognition for door opening systems |
| title_fullStr | Speaker recognition for door opening systems |
| title_full_unstemmed | Speaker recognition for door opening systems |
| title_short | Speaker recognition for door opening systems |
| title_sort | Speaker recognition for door opening systems |
| topic | Besides Communication tool Deep learning model |
| topic_facet | Besides Communication tool Deep learning model |
| url | http://hdl.handle.net/10198/29072 |
| visible | 1 |