Models of Visually Grounded Speech Signal Pay Attention To Nouns: a Bilingual Experiment on English and Japanese
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Abstract (arXiv)
We investigate the behaviour of attention in neural models of visually grounded speech trained on two languages: English and Japanese. Experimental results show that attention focuses on nouns and this behaviour holds true for two very typologically different languages. We also draw parallels between artificial neural attention and human attention and show that neural attention focuses on word endings as it has been theorised for human attention. Finally, we investigate how two visually grounded monolingual models can be used to perform cross-lingual speech-to-speech retrieval. For both languages, the enriched bilingual (speech-image) corpora with part-of-speech tags and forced alignments are distributed to the community for reproducible research.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Feb 2019.
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Citing papers per year: 2021: 6; 2022: 4; 2023: 1; 2024: 0; 2025: 0; 2026: 2.
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- 2022Self-Supervised Speech Representation Learning: A Reviewcited by 258
- 2021Visually grounded models of spoken language: A survey of datasets, architectures and evaluation techniquescited by 32
- 2021Attention-Based Keyword Localisation in Speech using Visual Groundingcited by 13
- 2021Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigationcited by 12
- 2023Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Modelcited by 11
- 2022M-SpeechCLIP: Leveraging Large-Scale, Pre-Trained Models for Multilingual Speech to Image Retrievalcited by 9
- 2021Cascaded Multilingual Audio-Visual Learning from Videoscited by 8
- 2022Learning English with Peppa Pigcited by 8
- 2022Keyword localisation in untranscribed speech using visually grounded speech modelscited by 7
- 2021ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 editioncited by 6
- 2021Spoken ObjectNet: A Bias-Controlled Spoken Caption Datasetcited by 2
- 2026Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priorscited by 1
- 2026Mapping Written Words to Spoken Words in a Different Language Using Only Visual Grounding27 Aug 2026
- 2026Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priors9 Mar 2026
- 2023Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model19 May 2023
- 2022M-SpeechCLIP: Leveraging Large-Scale, Pre-Trained Models for Multilingual Speech to Image Retrieval2 Nov 2022
- 2022Self-Supervised Speech Representation Learning: A Review21 May 2022
- 2022Learning English with Peppa Pig25 Feb 2022
- 2022Keyword localisation in untranscribed speech using visually grounded speech models2 Feb 2022
- 2021Cascaded Multilingual Audio-Visual Learning from Videos8 Nov 2021
- 2021Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset14 Oct 2021
- 2021Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigation29 Sep 2021
- 2021ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 edition14 Jul 2021
- 2021Attention-Based Keyword Localisation in Speech using Visual Grounding16 Jun 2021
- 2022Self-Supervised Speech Representation Learning: A Reviewcited by 258
- 2021Visually grounded models of spoken language: A survey of datasets, architectures and evaluation techniquescited by 32
- 2021Attention-Based Keyword Localisation in Speech using Visual Groundingcited by 13
- 2021Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigationcited by 12
- 2023Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Modelcited by 11
- 2022M-SpeechCLIP: Leveraging Large-Scale, Pre-Trained Models for Multilingual Speech to Image Retrievalcited by 9
- 2021Cascaded Multilingual Audio-Visual Learning from Videoscited by 8
- 2022Learning English with Peppa Pigcited by 8
- 2022Keyword localisation in untranscribed speech using visually grounded speech modelscited by 7
- 2021ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 editioncited by 6
- 2021Spoken ObjectNet: A Bias-Controlled Spoken Caption Datasetcited by 2
- 2026Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priorscited by 1
12 most cited of 13 All 13 (JSON)
- 2026Mapping Written Words to Spoken Words in a Different Language Using Only Visual Grounding27 Aug 2026
- 2026Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priors9 Mar 2026
- 2023Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model19 May 2023
- 2022M-SpeechCLIP: Leveraging Large-Scale, Pre-Trained Models for Multilingual Speech to Image Retrieval2 Nov 2022
- 2022Self-Supervised Speech Representation Learning: A Review21 May 2022
- 2022Learning English with Peppa Pig25 Feb 2022
- 2022Keyword localisation in untranscribed speech using visually grounded speech models2 Feb 2022
- 2021Cascaded Multilingual Audio-Visual Learning from Videos8 Nov 2021
- 2021Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset14 Oct 2021
- 2021Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigation29 Sep 2021
- 2021ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 edition14 Jul 2021
- 2021Attention-Based Keyword Localisation in Speech using Visual Grounding16 Jun 2021
12 newest of 13 All 13 (JSON)