Automatic Speech Recognition (ASR) System using convolutional and Recurrent neural Network Approach
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022 -- 9 June 2022 through 11 June 2022 -- Ankara -- 180434
Anahtar Kelimeler
AI model, LSTM model, speech recognition
Kaynak
HORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
