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Temporal Processing Neural Networks for Speech Recognition
| dc.contributor.author | Ivanov, A. V. | ru |
| dc.contributor.author | Petrowsky, A. A. | ru |
| dc.coverage.spatial | Brest | ru |
| dc.date.accessioned | 2021-07-27T08:09:26Z | |
| dc.date.available | 2021-07-27T08:09:26Z | |
| dc.date.issued | 1999 | |
| dc.identifier.citation | Ivanov, A. V. Temporal Processing Neural Networks for Speech Recognition / A. V. Ivanov, A. A. Petrowsky // International Conference on Neural Networks and Artificial Intelligence ICNNAI'99 = Международная конференция по нейронным сетям и искусственному интеллекту ICNNAI'99 : Proceedings, Brest, Belarus, 12–15 October 1999 / Brest Polytechnic Institute, Department of Computers and Laboratory of Artificial Neural Networks, Belarus Special Interest Group of International Neural NetWork Society, International Neural NetWork Society, Belarusian State University of Informatics and Radioelectronics (Belarus), Belarusian Academy of Sciences, Institute of Engineering Cybemetics (Belarus), Universidad Politechnica de Valencia (Spain), Institute of Computer Information Technologies (Ukraine, Ternopil) ; ed. V. Golovko. – Brest : BPI, 1999. – P. 117–125. | ru |
| dc.identifier.uri | https://rep.bstu.by/handle/data/20645 | |
| dc.description.abstract | Application of the temporal processing neural networks (TPNNs) to the speech recognition is justifled by the nature of the task. Indeed ASR is a sequence recognition problem and assumes incorporation of time into decision process. Static models treat elements of sequence as independent patterns, which is clearly unrealistic. On the other hand temporal Processing nets, built on the basis of multilayer perceptrons give us a hope to dismiss this assumption. | ru |
| dc.language.iso | en | ru |
| dc.publisher | BPI | ru |
| dc.title | Temporal Processing Neural Networks for Speech Recognition | ru |
| dc.type | Научный доклад (Working Paper) | ru |
