A system dedicated to Polish automatic speech recognition – overview of solutions

• Autorzy: Pondel-Sycz Karolina , Bilski Piotr

Identyfikatory

DOI

10.24425/bpasts.2024.149818

• Języki publikacji: EN

Abstrakty

EN

The paper presents the analysis of modern Artificial Intelligence algorithms for the automated system supporting human beings during their conversation in Polish language. Their task is to perform Automatic Speech Recognition (ASR) and process it further, for instance fill the computer-based form or perform the Natural Language Processing (NLP) to assign the conversation to one of predefined categories. The State-of-the-Art review is required to select the optimal set of tools to process speech in the difficult conditions, which degrade accuracy of ASR. The paper presents the top-level architecture of the system applicable for the task. Characteristics of Polish language are discussed. Next, existing ASR solutions and architectures with the End-To-End (E2E) deep neural network (DNN) based ASR models are presented in detail. Differences between Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) and Transformers in the context of ASR technology are also discussed.

Słowa kluczowe

EN

automatic speech recognition; deep neural network; transformer; conformer

PL

automatyczne rozpoznawanie mowy; głębokie sieci neuronowe; transformator; konformer

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2024
• Tom: Vol. 72, nr 4
• Strony: art. no. e149818
• Opis fizyczny: Bibliogr. 86 poz., rys., tab.

Twórcy

autor

Pondel-Sycz Karolina

• The Faculty of Electronics and Information Technology on Warsaw University of Technology, Nowowiejska 15/19 Av., 00-665 Warsaw, Poland

• https://orcid.org/0000-0002-0219-3649

autor

Bilski Piotr

• The Faculty of Electronics and Information Technology on Warsaw University of Technology, Nowowiejska 15/19 Av., 00-665 Warsaw, Poland

• https://orcid.org/0000-0002-5463-9411

Bibliografia

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