Standard

What Causes Phonetic Reduction in Russian Speech : New Evidence from Machine Learning Algorithms. / Dayter, Maria; Riekhakaynen, Elena.

Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings. ed. / Alexey Karpov; Rodmonga Potapova. Springer Nature, 2021. p. 146-156 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12997 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Dayter, M & Riekhakaynen, E 2021, What Causes Phonetic Reduction in Russian Speech: New Evidence from Machine Learning Algorithms. in A Karpov & R Potapova (eds), Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12997 LNAI, Springer Nature, pp. 146-156, 23rd International Conference on Speech and Computer, SPECOM 2021, Virtual, Online, Russian Federation, 27/09/21. https://doi.org/10.1007/978-3-030-87802-3_14

APA

Dayter, M., & Riekhakaynen, E. (2021). What Causes Phonetic Reduction in Russian Speech: New Evidence from Machine Learning Algorithms. In A. Karpov, & R. Potapova (Eds.), Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings (pp. 146-156). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12997 LNAI). Springer Nature. https://doi.org/10.1007/978-3-030-87802-3_14

Vancouver

Dayter M, Riekhakaynen E. What Causes Phonetic Reduction in Russian Speech: New Evidence from Machine Learning Algorithms. In Karpov A, Potapova R, editors, Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings. Springer Nature. 2021. p. 146-156. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-87802-3_14

Author

Dayter, Maria ; Riekhakaynen, Elena. / What Causes Phonetic Reduction in Russian Speech : New Evidence from Machine Learning Algorithms. Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings. editor / Alexey Karpov ; Rodmonga Potapova. Springer Nature, 2021. pp. 146-156 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{d7feacd26e2e4deb9433b0a0cb6f9ea4,
title = "What Causes Phonetic Reduction in Russian Speech: New Evidence from Machine Learning Algorithms",
abstract = "In this paper, we describe the second stage of the study aimed at describing the factors that influence the phonetic reduction of words in Russian speech using machine learning algorithms. We discuss the limitations of the first stage of our study and try to overcome some of them by increasing the dataset and using new algorithms such as random forest, gradient boosting, and perceptron. We used the texts from the Corpus of Russian Speech as the data. The dataset was divided into two separate datasets: one consisted of single words and the other contained multiword units from our corpus. According to the results, for single words the most important features turned out to be the number of syllables and whether the word is an adjective as they were chosen by all algorithms. For the multiword units, the main features were the number of syllables, frequency in Russian spoken texts (in ipm), and token frequency in a given text. In our further research, we are going to expand the dataset and look closer on such features as text type and token frequency in a given text.",
keywords = "Machine learning, Phonetic reduction, Russian, Speech",
author = "Maria Dayter and Elena Riekhakaynen",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 23rd International Conference on Speech and Computer, SPECOM 2021 ; Conference date: 27-09-2021 Through 30-09-2021",
year = "2021",
doi = "10.1007/978-3-030-87802-3_14",
language = "English",
isbn = "9783030878016",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "146--156",
editor = "Alexey Karpov and Rodmonga Potapova",
booktitle = "Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings",
address = "Germany",
url = "http://specom.nw.ru/2021/",

}

RIS

TY - GEN

T1 - What Causes Phonetic Reduction in Russian Speech

T2 - 23rd International Conference on Speech and Computer, SPECOM 2021

AU - Dayter, Maria

AU - Riekhakaynen, Elena

N1 - Conference code: 23

PY - 2021

Y1 - 2021

N2 - In this paper, we describe the second stage of the study aimed at describing the factors that influence the phonetic reduction of words in Russian speech using machine learning algorithms. We discuss the limitations of the first stage of our study and try to overcome some of them by increasing the dataset and using new algorithms such as random forest, gradient boosting, and perceptron. We used the texts from the Corpus of Russian Speech as the data. The dataset was divided into two separate datasets: one consisted of single words and the other contained multiword units from our corpus. According to the results, for single words the most important features turned out to be the number of syllables and whether the word is an adjective as they were chosen by all algorithms. For the multiword units, the main features were the number of syllables, frequency in Russian spoken texts (in ipm), and token frequency in a given text. In our further research, we are going to expand the dataset and look closer on such features as text type and token frequency in a given text.

AB - In this paper, we describe the second stage of the study aimed at describing the factors that influence the phonetic reduction of words in Russian speech using machine learning algorithms. We discuss the limitations of the first stage of our study and try to overcome some of them by increasing the dataset and using new algorithms such as random forest, gradient boosting, and perceptron. We used the texts from the Corpus of Russian Speech as the data. The dataset was divided into two separate datasets: one consisted of single words and the other contained multiword units from our corpus. According to the results, for single words the most important features turned out to be the number of syllables and whether the word is an adjective as they were chosen by all algorithms. For the multiword units, the main features were the number of syllables, frequency in Russian spoken texts (in ipm), and token frequency in a given text. In our further research, we are going to expand the dataset and look closer on such features as text type and token frequency in a given text.

KW - Machine learning

KW - Phonetic reduction

KW - Russian

KW - Speech

UR - http://www.scopus.com/inward/record.url?scp=85116342082&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/f8bceca4-1368-359d-a283-305fa6528c35/

U2 - 10.1007/978-3-030-87802-3_14

DO - 10.1007/978-3-030-87802-3_14

M3 - Conference contribution

AN - SCOPUS:85116342082

SN - 9783030878016

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 146

EP - 156

BT - Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings

A2 - Karpov, Alexey

A2 - Potapova, Rodmonga

PB - Springer Nature

Y2 - 27 September 2021 through 30 September 2021

ER -

ID: 87566335