Automatic Detection of Backchannels in Russian Dialogue Speech

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. ed. / Alexey Karpov; Rodmonga Potapova. Cham : Springer Nature, 2020. p. 204-213 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12335 LNAI).

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

Harvard

Kholiavin, P, Mamushina, A, Kocharov, D & Kachkovskaia, T 2020, Automatic Detection of Backchannels in Russian Dialogue Speech. in A Karpov & R Potapova (eds), 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), vol. 12335 LNAI, Springer Nature, Cham, pp. 204-213, 22nd International Conference on Speech and Computer, St. Petersburg, Russian Federation, 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. In A. Karpov, & R. Potapova (Eds.), Speech and Computer - 22nd International Conference, SPECOM 2020, Proceedings: 22nd International Conference, SPECOM 2020, St. Petersburg, Russia, October 7–9, 2020, Proceedings (pp. 204-213). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 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. In Karpov A, Potapova R, editors, 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. p. 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. editor / Alexey Karpov ; Rodmonga Potapova. Cham : Springer Nature, 2020. pp. 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

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 -

ID: 69802810