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.
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The research is supported by Russian Science Foundation (Project 19-78-10046 “Phonetic manifestations of communication accommodation in dialogue”).
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Kholiavin, P., Mamushina, A., Kocharov, D., Kachkovskaia, T. (2020). Automatic Detection of Backchannels in Russian Dialogue Speech. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2020. Lecture Notes in Computer Science(), vol 12335. Springer, Cham. https://doi.org/10.1007/978-3-030-60276-5_21
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DOI: https://doi.org/10.1007/978-3-030-60276-5_21
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