See what I'm saying? Comparing Intelligent Personal Assistant use for Native and Non-Native Language Speakers
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Abstract (arXiv)
Limited linguistic coverage for Intelligent Personal Assistants (IPAs) means that many interact in a non-native language. Yet we know little about how IPAs currently support or hinder these users. Through native (L1) and non-native (L2) English speakers interacting with Google Assistant on a smartphone and smart speaker, we aim to understand this more deeply. Interviews revealed that L2 speakers prioritised utterance planning around perceived linguistic limitations, as opposed to L1 speakers prioritising succinctness because of system limitations. L2 speakers see IPAs as insensitive to linguistic needs resulting in failed interaction. L2 speakers clearly preferred using smartphones, as visual feedback supported diagnoses of communication breakdowns whilst allowing time to process query results. Conversely, L1 speakers preferred smart speakers, with audio feedback being seen as sufficient. We discuss the need to tailor the IPA experience for L2 users, emphasising visual feedback whilst reducing the burden of language production.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Jun 2020.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 1; 2025: 0; 2026: 2.
7 papers.
- 2021Quantifying Bias in Automatic Speech Recognitioncited by 50
- 2022Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Reviewcited by 19
- 2021Widening Access to Applied Machine Learning with TinyMLcited by 17
- 2024Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistantscited by 6
- 2022Bilingual by default: Voice Assistants and the role of code-switching in creating a bilingual user experiencecited by 0
- 2026Unheard in the Digital Age: Rethinking AI Bias and Speech Diversitycited by 0
- 2026Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Resultscited by 0
- 2026Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results21 Jul 2026
- 2026Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity26 Jan 2026
- 2024Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants14 May 2024
- 2022Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review17 Nov 2022
- 2022Bilingual by default: Voice Assistants and the role of code-switching in creating a bilingual user experience20 Jun 2022
- 2021Widening Access to Applied Machine Learning with TinyML7 Jun 2021
- 2021Quantifying Bias in Automatic Speech Recognition28 Mar 2021