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Shaping unbalanced multi-party interactions through adaptive robot backchannels
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0003-4472-4732
KTH.
KTH.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0003-4532-014X
2022 (English)In: IVA 2022 - Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents, Association for Computing Machinery, Inc , 2022Conference paper, Published paper (Refereed)
Abstract [en]

Non-verbal cues used in human communication have shown to be efficient in shaping speaking interactions. When applied to virtual agents or social robots, results imply that a similar effect is expected in dyad settings. In this study, we explore how encouraging, vocal and non-vocal, backchannels can stimulate speaking participation in a game-based multi-party interaction, where unbalanced contribution is expected. We design the study using a social robot, taking part in a language game with native speakers and language learners, to evaluate how an adaptive generation of backchannels, that targets the least speaking participant to encourage more speaking contribution, affects the group and individual participant's behavior. We report results from experiments with 30 subjects divided in pairs assigned to the adaptive (encouraging) and (neutral) control condition. Our results show that the speaking participation of the least active speaker increases significantly when the robot uses an adaptive backchanneling strategy. At the same time, the participation of the more active speaker slightly decreases, which causes the combined speaking time of both participants to be similar between the Control and Experimental conditions. The adaptive strategy further leads to a 50% decrease in the difference in speaker shares between the two participants (indicating a more balanced participation) compared to the Control condition. However, this distribution between speaker ratios is not significantly different from the Control.

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc , 2022.
Keywords [en]
encouraging behavior, human-robot interaction, non-verbal behavior, second language learning, social robot, Social robots, Back channels, Condition, Humans-robot interactions, Language learning, Multiparty interaction, Non-verbal behaviours, Second language, Machine design
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:kth:diva-327284DOI: 10.1145/3514197.3549680Scopus ID: 2-s2.0-85138738227OAI: oai:DiVA.org:kth-327284DiVA, id: diva2:1758969
Conference
IVA 2022 - Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents
Note

QC 20230524

Available from: 2023-05-24 Created: 2023-05-24 Last updated: 2024-02-26Bibliographically approved
In thesis
1. Robots Beyond Borders: The Role of Social Robots in Spoken Second Language Practice
Open this publication in new window or tab >>Robots Beyond Borders: The Role of Social Robots in Spoken Second Language Practice
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Robotar bortom gränser : Sociala robotars roll i talat andraspråk
Abstract [en]

This thesis investigates how social robots can support adult second language (L2) learners in improving conversational skills. It recognizes the challenges inherent in adult L2 learning, including increased cognitive demands and the unique motivations driving adult education. While social robots hold potential for natural interactions and language education, research into conversational skill practice with adult learners remains underexplored. Thus, the thesis contributes to understanding these conversational dynamics, enhancing speaking practice, and examining cultural perspectives in this context.

To begin, this thesis investigates robot-led conversations with L2 learners, examining how learners respond to moments of uncertainty. The research reveals that when faced with uncertainty, learners frequently seek clarification, yet many remain unresponsive. As a result, effective strategies are required from robot conversational partners to address this challenge. These interactions are then used to evaluate the performance of off-the-shelf Automatic Speech Recognition (ASR) systems. The assessment highlights that speech recognition for L2 speakers is not as effective as for L1 speakers, with performance deteriorating for both groups during social conversations. Addressing these challenges is imperative for the successful integration of robots in conversational practice with L2 learners.

The thesis then explores the potential advantages of employing social robots in collaborative learning environments with multi-party interactions. It delves into strategies for improving speaking practice, including the use of non-verbal behaviors to encourage learners to speak. For instance, a robot's adaptive gazing behavior is used to effectively balance speaking contributions between L1 and L2 pairs of participants. Moreover, an adaptive use of encouraging backchannels significantly increases the speaking time of L2 learners.

Finally, the thesis highlights the importance of further research on cultural aspects in human-robot interactions. One study reveals distinct responses among various socio-cultural groups in interaction between L1 and L2 participants. For example, factors such as gender, age, extroversion, and familiarity with robots influence conversational engagement of L2 speakers. Additionally, another study investigates preconceptions related to the appearance and accents of nationality-encoded (virtual and physical) social robots. The results indicate that initial perceptions may lead to negative preconceptions, but that these perceptions diminish after actual interactions.

Despite technical limitations, social robots provide distinct benefits in supporting educational endeavors. This thesis emphasizes the potential of social robots as effective facilitators of spoken language practice for adult learners, advocating for continued exploration at the intersection of language education, human-robot interaction, and technology.

Abstract [sv]

Denna avhandling undersöker hur sociala robotar kan ge vuxna andraspråks\-inlärare stöd att förbättra sin konversationsförmåga på svenska. Andraspråks\-inlärning för vuxna, särskilt i migrationskontext, är mer komplext än för barn, bland annat på grund av att förutsättningarna för språkinlärning försämras med åren och att drivkrafterna ofta är andra. Sociala robotar har stor potential inom språkundervisning för att träna naturliga samtal, men fortfarande har lite forskning om hur robotar kan öva konversation med vuxna elever genomförts. Därför bidrar avhandlingen till att förstå samtal mellan andraspråksinlärare och robotar, förbättra dessa samtalsövningar och undersöka hur kulturella faktorer påverkar interaktionen.

Till att börja med undersöker avhandlingen hur andraspråkselever reagerar då de blir förbryllade eller osäkra i robotledda konversationsövningar. Resultaten visar att eleverna ofta försöker få roboten att ge förtydliganden när de är osäkra, men att de ibland helt enkelt inte svarar något alls, vilket innebär att roboten behöver kunna hantera sådana situationer. Konversationerna mellan andraspråksinlärare och en robot har även använts för att undersöka hur väl ledande system för taligenkänning kan tolka det adraspråkstalare säger. Det kan konstateras att systemen har väsentligt större svårigheter att känna igen andraspråkstalare än personer med svensk bakgrund, samt att de har utmananingar att tolka såväl svenska talare som andraspråkselever i friare sociala konversationer, vilket måste hanteras när robotar ska användas i samtalsövningar med andraspråkselever.

Avhandlingen undersöker sedan strategier för att uppmuntra andraspråks\-elever att prata mer och för att fördela ordet jämnare i trepartsövningar där två personer samtalar med roboten. Strategierna går ut på att modifiera hur roboten tittar på de två personerna eller ger icke-verbal återkoppling (hummanden) för att signalera förståelse och intresse för det eleverna säger.

Slutligen belyser avhandlingen vikten av ytterligare forskning om kulturella aspekter i interaktioner mellan människa och robot. En studie visar att faktorer som kön, ålder, tidigare erfarenhet av robotar och hur extrovert eleven är påverkar både hur mycket olika personer talar och hur de svarar på robotens försök att uppmuntra dem att tala mer genom icke-verbala signaler.

En andra studie undersöker om och hur förutfattade meningar relaterade till utseende och uttal påverkar hur människor uppfattar (virtuella och fysiska) sociala robotar som givits egenskaper (röst och ansikte) som kan kopplas till olika nationella bakgrunder. Resultaten visar att människors första intryck av en kulturellt färgad robot speglar förutfattade meningar, men att denna uppfattning inte alls får samma genomslag när personer faktiskt interagerat med roboten i ett realistiskt sammanhang.

En huvudsaklig slutsats i avhandlingen är att sociala robotar, trots att tekniska begränsningar finns kvar, har tydliga fördelar som kan utnyttjas inom utbildning. Specifikt betonar avhandlingen potentialen hos sociala robotar att leda samtalsövningar för vuxna andraspråkselever och förespråkar fortsatt forskning i skärningspunkten mellan språkundervisning, människa-robotinteraktion och teknik.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2024. p. 91
Series
TRITA-EECS-AVL ; 2024:23
Keywords
Conversations, gaze, backchannels, multi-party, accent, culture, Samtal, blick, återkoppling, gruppdynamik, brytning, kultur
National Category
Robotics Language Technology (Computational Linguistics)
Research subject
Speech and Music Communication
Identifiers
urn:nbn:se:kth:diva-343863 (URN)978-91-8040-858-5 (ISBN)
Public defence
2024-03-22, https://kth-se.zoom.us/j/65591848998, F3, Lindstedtsvägen 26, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20240226

Available from: 2024-02-26 Created: 2024-02-26 Last updated: 2024-03-06Bibliographically approved

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Cumbal, RonaldKazzi, Daniel AlexanderWinberg, VincentEngwall, Olov

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