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Daryn Kalym

dblp:237/8500 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 44% Learning and educational technologies · 44% Human-AI interaction · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Learning and educational technologies
language learning
0.412019
Similarity Attraction for Robot's Dialect in Language Learning Using Social Robots · HRI 2019
Human-robot interaction
social robot
0.412019
Similarity Attraction for Robot's Dialect in Language Learning Using Social Robots · HRI 2019
Human-AI interaction
speech synthesis
0.112019
Similarity Attraction for Robot's Dialect in Language Learning Using Social Robots · HRI 2019

Methods — techniques the papers use, named apart from their topics

user study · 0.4
YearPublicationVenuePosition
2019 Similarity Attraction for Robot's Dialect in Language Learning Using Social Robots
abstract
The gradual transition towards the Kazakh language in the Republic of Kazakhstan raises the emergence of applying new technologies for learning the language. Considering the fact that the Kazakh language has dialectal forms, it is important to investigate how these language features would affect the interaction with the synthesized speech of a robot or a computer program. This paper presents a preliminary study exploring the effect of dialectal language on the human-robot interaction in an education-oriented environment. Participants were involved in the interaction with two different robots with pre-programmed language dialectal patterns - South and non-South, to learn new vocabulary. Findings show that there is a low significance in correlation, however, it is suggested that a small sample size led to the obtained results.
Askarbek Pazylbekov, Daryn Kalym, Anuar Otynshin, Anara Sandygulova
HRI2