Notice of Violation of IEEE Publication Principles: Automatic Prosody Markup Based on Fundamental Frequency

Abstract

Notice of Violation of IEEE Publication Principles<br><br> "Automatic Prosody Markup Based on Fundamental Frequency,"<br> by A. Shilkov, S. Ivanov, M. Sipatov and V. Golodov,<br> in the Proceedings of the International Russian Automation Conference (RusAutoCon), 2021, pp. 751-756<br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.<br><br> This paper contains a copied content from the article cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br> “Multiple F0 Estimation in Vocal Ensembles using Convolutional Neural Networks”<br> by Helena Cuesta; Brian McFee; Emilia Gomez<br> in the Proceedings of ISMIR 2020<br><br> <br/> Prosody can be referred to those elements of speech that represent the properties of syllables and larger units of speech. Prosody also includes individual linguistic functions such as rhythm, accent, and intonation. Prosody makes it possible to identify the speaker's vocal personality or the characteristics of utterances, such as the speaker's emotional state or the style of utterance. Studying dialects or languages often requires prosody markup. The article is devoted to automatic prosody markup based on the fundamental frequency of the utterance. With the extraction of the fundamental frequency being the main challenge multiple methods are reviewed such as SOTA neural networks and more conservative algorithms. After applying one of these methods, SLAM markup is obtained. In the end markups based on differently obtained fundamental frequencies are compared.