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Comparing Command Construction in Native and Non-Native Speaker IPA Interaction through Conversation Analysis

Yunhan Wu, Martin Porcheron Orcid Logo, Philip Doyle, Justin Edwards, Daniel Rough, Orla Cooney, Anna Bleakley, Leigh Clark Orcid Logo, Benjamin Cowan

4th Conference on Conversational User Interfaces

Swansea University Authors: Martin Porcheron Orcid Logo, Leigh Clark Orcid Logo

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DOI (Published version): 10.1145/3543829.3543839

Abstract

Intelligent Personal Assistants (IPAs) are limited in the languages they support, meaning many people are left to interact using a non-native language. Yet, we know little about how people interact with IPAs in this way. Through a conversation analysis (CA) perspective, we examine native (L1) and no...

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Published in: 4th Conference on Conversational User Interfaces
ISBN: 978-1-4503-9739-1
Published: New York, NY, USA ACM 2022
URI: https://cronfa.swan.ac.uk/Record/cronfa60212
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Abstract: Intelligent Personal Assistants (IPAs) are limited in the languages they support, meaning many people are left to interact using a non-native language. Yet, we know little about how people interact with IPAs in this way. Through a conversation analysis (CA) perspective, we examine native (L1) and non-native (L2) English speaker interactions with Google Assistant, comparing how both user groups produce IPA commands. Our work shows that L1 and L2 speakers similarly used pauses, partial or complete repetition, and hyper-articulation when constructing commands. However, L2 speakers tended to experience issues in lexical access, syntactic construction and pronunciation, resulting in the use of code-mixing, increased pause lengths and off-task rehearsal to help generate commands. We consider reasons for such effects, whilst exploring ways to design IPA interaction to ensure it is sensitive to L2 challenges in command production.
Keywords: speech interface, voice user interface, intelligent personal assistants, non-native speakers
College: College of Science
Funders: This work was conducted with the financial support of the UCD China Scholarship Council (CSC) Scheme grant No. 201908300016, Science Foundation Ireland ADAPT Centre under Grant No. 13/RC/2106 and the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (D-REAL) under Grant No.18/CRT/6224.