Automatic Speech Recognition (ASR) System using convolutional and Recurrent neural Network Approach
| dc.contributor.author | Al-Mansoori, K.W. | |
| dc.contributor.author | Cakmak, M. | |
| dc.date.accessioned | 2024-09-29T16:20:56Z | |
| dc.date.available | 2024-09-29T16:20:56Z | |
| dc.date.issued | 2022 | |
| dc.department | Karabük Üniversitesi | en_US |
| dc.description | 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022 -- 9 June 2022 through 11 June 2022 -- Ankara -- 180434 | en_US |
| dc.description.abstract | Nowadays, speech recognition is an active research field, where various deep neural architectures are explored. The published successful models are optimized on massive, transcribed datasets, most of which are closed. A deep neural network solves two closely related tasks. It learns to recognize phonemes and formulate grammar rules at the same time. A model can parallel and accurately build both of them when a training corpus is large enough. However, inflected languages such as Polish contain much more grammar rules to define than in the case of English. Therefore, to achieve comparable results in the Polish language, the corpus must be substantially larger than the one presented for the English language. In contrast, to build more massive datasets, we present the Synthetic Boosted Model, which is an attempt to use synthetic data to enrich more profound the implicit language model. In the presented work, we propose the new model architecture, the new objective function, and the new training policy. © 2022 IEEE. | en_US |
| dc.identifier.doi | 10.1109/HORA55278.2022.9799877 | |
| dc.identifier.isbn | 978-166546835-0 | |
| dc.identifier.scopus | 2-s2.0-85133957024 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://doi.org/10.1109/HORA55278.2022.9799877 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14619/9431 | |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | HORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | AI model | en_US |
| dc.subject | LSTM model | en_US |
| dc.subject | speech recognition | en_US |
| dc.title | Automatic Speech Recognition (ASR) System using convolutional and Recurrent neural Network Approach | en_US |
| dc.type | Conference Object | en_US |
