Code-Mixed Street Address Recognition and Accent Adaptation for Voice-Activated Navigation Services
<p dir="ltr">This study presents the development of a real-time application-specific Automatic Speech Recognition (ASR) system for voice-activated navigation services. The system is designed to recognize Urdu-English code-mixed street addresses, which is challenging due to their comp...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2024
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| _version_ | 1864513539945791488 |
|---|---|
| author | Syed Meesam Raza Naqvi (22225462) |
| author2 | Muhammad Ali Tahir (6640979) Kamran Javed (21726248) Hassan Aqeel Khan (22225465) Ali Raza (3558965) Zubair Saeed (19325647) |
| author2_role | author author author author author |
| author_facet | Syed Meesam Raza Naqvi (22225462) Muhammad Ali Tahir (6640979) Kamran Javed (21726248) Hassan Aqeel Khan (22225465) Ali Raza (3558965) Zubair Saeed (19325647) |
| author_role | author |
| dc.creator.none.fl_str_mv | Syed Meesam Raza Naqvi (22225462) Muhammad Ali Tahir (6640979) Kamran Javed (21726248) Hassan Aqeel Khan (22225465) Ali Raza (3558965) Zubair Saeed (19325647) |
| dc.date.none.fl_str_mv | 2024-11-22T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3496617 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Code-Mixed_Street_Address_Recognition_and_Accent_Adaptation_for_Voice-Activated_Navigation_Services/30095377 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Urdu-English code-mixing roman Urdu addresses accent adaptation deep neural network Gaussian mixture models hidden Markov models voice-activated navigation Speech recognition Acoustics Vocabulary Speech coding Real-time systems Navigation Long short term memory Error analysis Switches Natural language processing |
| dc.title.none.fl_str_mv | Code-Mixed Street Address Recognition and Accent Adaptation for Voice-Activated Navigation Services |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This study presents the development of a real-time application-specific Automatic Speech Recognition (ASR) system for voice-activated navigation services. The system is designed to recognize Urdu-English code-mixed street addresses, which is challenging due to their complex nature and structure, especially in under-resourced languages such as Urdu. Two separate corpora are collected for ASR system development: Unicode Urdu consisting of general Urdu recordings of around 61.82 hours by 144 speakers and Roman Urdu-English code-mixed Addresses of around 16.89 hours by 20 speakers. The Unicode Urdu data is developed to provide acoustic models with general language understanding and code-mixed street addresses to provide code-mixing or switching coverage. The hybrid ASR system employed in this study plays a crucial role in addressing the multifaceted challenges of low-resource settings (only 16.89 hours of task-specific data), especially in the context of Urdu-English code-switching. The study compares various acoustic models, with mixed Time Delay Neural Network and Long Short-Term Memory (TDNN-LSTM) performing best with a Word Error Rate (WER), Character Error Rate (CER), and Sentence Error Rate (SER) of 4.02%, 0.8%, and 15.14% respectively, on random street addresses. In addition to testing street addresses, we performed accent-based and manual decoding testing on the developed ASR system. Results indicate the need to develop and deploy custom ASR systems for better accent adaptation and application-specific coverage.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3496617" target="_blank">https://dx.doi.org/10.1109/access.2024.3496617</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_641be73879a2241d48947d16557b7dcc |
| identifier_str_mv | 10.1109/access.2024.3496617 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30095377 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Code-Mixed Street Address Recognition and Accent Adaptation for Voice-Activated Navigation ServicesSyed Meesam Raza Naqvi (22225462)Muhammad Ali Tahir (6640979)Kamran Javed (21726248)Hassan Aqeel Khan (22225465)Ali Raza (3558965)Zubair Saeed (19325647)EngineeringElectronics, sensors and digital hardwareInformation and computing sciencesMachine learningUrdu-English code-mixingroman Urdu addressesaccent adaptationdeep neural networkGaussian mixture modelshidden Markov modelsvoice-activated navigationSpeech recognitionAcousticsVocabularySpeech codingReal-time systemsNavigationLong short term memoryError analysisSwitchesNatural language processing<p dir="ltr">This study presents the development of a real-time application-specific Automatic Speech Recognition (ASR) system for voice-activated navigation services. The system is designed to recognize Urdu-English code-mixed street addresses, which is challenging due to their complex nature and structure, especially in under-resourced languages such as Urdu. Two separate corpora are collected for ASR system development: Unicode Urdu consisting of general Urdu recordings of around 61.82 hours by 144 speakers and Roman Urdu-English code-mixed Addresses of around 16.89 hours by 20 speakers. The Unicode Urdu data is developed to provide acoustic models with general language understanding and code-mixed street addresses to provide code-mixing or switching coverage. The hybrid ASR system employed in this study plays a crucial role in addressing the multifaceted challenges of low-resource settings (only 16.89 hours of task-specific data), especially in the context of Urdu-English code-switching. The study compares various acoustic models, with mixed Time Delay Neural Network and Long Short-Term Memory (TDNN-LSTM) performing best with a Word Error Rate (WER), Character Error Rate (CER), and Sentence Error Rate (SER) of 4.02%, 0.8%, and 15.14% respectively, on random street addresses. In addition to testing street addresses, we performed accent-based and manual decoding testing on the developed ASR system. Results indicate the need to develop and deploy custom ASR systems for better accent adaptation and application-specific coverage.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3496617" target="_blank">https://dx.doi.org/10.1109/access.2024.3496617</a></p>2024-11-22T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3496617https://figshare.com/articles/journal_contribution/Code-Mixed_Street_Address_Recognition_and_Accent_Adaptation_for_Voice-Activated_Navigation_Services/30095377CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300953772024-11-22T15:00:00Z |
| spellingShingle | Code-Mixed Street Address Recognition and Accent Adaptation for Voice-Activated Navigation Services Syed Meesam Raza Naqvi (22225462) Engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Urdu-English code-mixing roman Urdu addresses accent adaptation deep neural network Gaussian mixture models hidden Markov models voice-activated navigation Speech recognition Acoustics Vocabulary Speech coding Real-time systems Navigation Long short term memory Error analysis Switches Natural language processing |
| status_str | publishedVersion |
| title | Code-Mixed Street Address Recognition and Accent Adaptation for Voice-Activated Navigation Services |
| title_full | Code-Mixed Street Address Recognition and Accent Adaptation for Voice-Activated Navigation Services |
| title_fullStr | Code-Mixed Street Address Recognition and Accent Adaptation for Voice-Activated Navigation Services |
| title_full_unstemmed | Code-Mixed Street Address Recognition and Accent Adaptation for Voice-Activated Navigation Services |
| title_short | Code-Mixed Street Address Recognition and Accent Adaptation for Voice-Activated Navigation Services |
| title_sort | Code-Mixed Street Address Recognition and Accent Adaptation for Voice-Activated Navigation Services |
| topic | Engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Urdu-English code-mixing roman Urdu addresses accent adaptation deep neural network Gaussian mixture models hidden Markov models voice-activated navigation Speech recognition Acoustics Vocabulary Speech coding Real-time systems Navigation Long short term memory Error analysis Switches Natural language processing |