Standard
Automatic Detection of Backchannels in Russian Dialogue Speech. / Kholiavin, Pavel ; Mamushina, Anna ; Kocharov, Daniil ; Kachkovskaia, Tatiana.
Speech and Computer - 22nd International Conference, SPECOM 2020, Proceedings: 22nd International Conference, SPECOM 2020, St. Petersburg, Russia, October 7–9, 2020, Proceedings. ред. / Alexey Karpov; Rodmonga Potapova. Cham : Springer Nature, 2020. стр. 204-213 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 12335 LNAI).
Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Harvard
Kholiavin, P, Mamushina, A
, Kocharov, D & Kachkovskaia, T 2020,
Automatic Detection of Backchannels in Russian Dialogue Speech. в A Karpov & R Potapova (ред.),
Speech and Computer - 22nd International Conference, SPECOM 2020, Proceedings: 22nd International Conference, SPECOM 2020, St. Petersburg, Russia, October 7–9, 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Том. 12335 LNAI, Springer Nature, Cham, стр. 204-213, 22nd International Conference on Speech and Computer, St. Petersburg, Российская Федерация,
7/10/20.
https://doi.org/10.1007/978-3-030-60276-5_21
APA
Kholiavin, P., Mamushina, A.
, Kocharov, D., & Kachkovskaia, T. (2020).
Automatic Detection of Backchannels in Russian Dialogue Speech. в A. Karpov, & R. Potapova (Ред.),
Speech and Computer - 22nd International Conference, SPECOM 2020, Proceedings: 22nd International Conference, SPECOM 2020, St. Petersburg, Russia, October 7–9, 2020, Proceedings (стр. 204-213). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 12335 LNAI). Springer Nature.
https://doi.org/10.1007/978-3-030-60276-5_21
Vancouver
Kholiavin P, Mamushina A
, Kocharov D, Kachkovskaia T.
Automatic Detection of Backchannels in Russian Dialogue Speech. в Karpov A, Potapova R, Редакторы, Speech and Computer - 22nd International Conference, SPECOM 2020, Proceedings: 22nd International Conference, SPECOM 2020, St. Petersburg, Russia, October 7–9, 2020, Proceedings. Cham: Springer Nature. 2020. стр. 204-213. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
https://doi.org/10.1007/978-3-030-60276-5_21
Author
Kholiavin, Pavel ; Mamushina, Anna
; Kocharov, Daniil ; Kachkovskaia, Tatiana. /
Automatic Detection of Backchannels in Russian Dialogue Speech. Speech and Computer - 22nd International Conference, SPECOM 2020, Proceedings: 22nd International Conference, SPECOM 2020, St. Petersburg, Russia, October 7–9, 2020, Proceedings. Редактор / Alexey Karpov ; Rodmonga Potapova. Cham : Springer Nature, 2020. стр. 204-213 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{117bfb64ffb54bfa84844285ef486d0b,
title = "Automatic Detection of Backchannels in Russian Dialogue Speech",
abstract = "This paper deals with acoustic properties of backchannels – those turns within a dialogue which do not convey information but signify that the speaker is listening to his/her interlocutor (uh-huh, hm etc.). The research is based on a Russian corpus of dialogue speech, SibLing, a part of which (339 min of speech) was manually segmented into backchannels and non-backchannels. Then, a number of acoustic parameters was calculated: duration, intensity, fundamental frequency, and pause duration. Our data have shown that in Russian speech backchannels are shorter and have lower loudness and pitch than non-backchannels. After that, two classifiers were tested: CART and SVM. The highest efficiency was achieved using SVM (F 1 = 0.651) and the following feature set: duration, maximum fundamental frequency, melodic slope. The most valuable feature was duration.",
keywords = "dialogue speech, backchannel, turn-taking, speech acoustics, Russian, Backchannel, Dialogue speech, Russian, Speech acoustics, Turn-taking",
author = "Pavel Kholiavin and Anna Mamushina and Daniil Kocharov and Tatiana Kachkovskaia",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 22nd International Conference on Speech and Computer, SPECOM 2020 ; Conference date: 07-10-2020 Through 09-10-2020",
year = "2020",
doi = "https://doi.org/10.1007/978-3-030-60276-5_21",
language = "English",
isbn = "978-3-030-60275-8",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "204--213",
editor = "Karpov, {Alexey } and Potapova, {Rodmonga }",
booktitle = "Speech and Computer - 22nd International Conference, SPECOM 2020, Proceedings",
address = "Germany",
url = "http://specom.nw.ru/2020/program/SPECOM-ICR2020-Conference-Program-06102020.pdf",
}
RIS
TY - GEN
T1 - Automatic Detection of Backchannels in Russian Dialogue Speech
AU - Kholiavin, Pavel
AU - Mamushina, Anna
AU - Kocharov, Daniil
AU - Kachkovskaia, Tatiana
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - This paper deals with acoustic properties of backchannels – those turns within a dialogue which do not convey information but signify that the speaker is listening to his/her interlocutor (uh-huh, hm etc.). The research is based on a Russian corpus of dialogue speech, SibLing, a part of which (339 min of speech) was manually segmented into backchannels and non-backchannels. Then, a number of acoustic parameters was calculated: duration, intensity, fundamental frequency, and pause duration. Our data have shown that in Russian speech backchannels are shorter and have lower loudness and pitch than non-backchannels. After that, two classifiers were tested: CART and SVM. The highest efficiency was achieved using SVM (F 1 = 0.651) and the following feature set: duration, maximum fundamental frequency, melodic slope. The most valuable feature was duration.
AB - This paper deals with acoustic properties of backchannels – those turns within a dialogue which do not convey information but signify that the speaker is listening to his/her interlocutor (uh-huh, hm etc.). The research is based on a Russian corpus of dialogue speech, SibLing, a part of which (339 min of speech) was manually segmented into backchannels and non-backchannels. Then, a number of acoustic parameters was calculated: duration, intensity, fundamental frequency, and pause duration. Our data have shown that in Russian speech backchannels are shorter and have lower loudness and pitch than non-backchannels. After that, two classifiers were tested: CART and SVM. The highest efficiency was achieved using SVM (F 1 = 0.651) and the following feature set: duration, maximum fundamental frequency, melodic slope. The most valuable feature was duration.
KW - dialogue speech
KW - backchannel
KW - turn-taking
KW - speech acoustics
KW - Russian
KW - Backchannel
KW - Dialogue speech
KW - Russian
KW - Speech acoustics
KW - Turn-taking
UR - https://link.springer.com/book/10.1007/978-3-030-60276-5
UR - http://www.scopus.com/inward/record.url?scp=85092902548&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/49e0c6d1-35c5-3351-8283-cb0e7e5b47c8/
U2 - https://doi.org/10.1007/978-3-030-60276-5_21
DO - https://doi.org/10.1007/978-3-030-60276-5_21
M3 - Conference contribution
SN - 978-3-030-60275-8
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 204
EP - 213
BT - Speech and Computer - 22nd International Conference, SPECOM 2020, Proceedings
A2 - Karpov, Alexey
A2 - Potapova, Rodmonga
PB - Springer Nature
CY - Cham
T2 - 22nd International Conference on Speech and Computer
Y2 - 7 October 2020 through 9 October 2020
ER -
