AR
A. Rawat
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Odour-cued memory retrieval has emerged as a potential intervention for people with dementia due to the unique properties of odour-evoked memories. The design and application of technologies that integrate this strategy require knowing which odours are meaningful to a given person, motivating the systematic modelling of odour-event associations across the lifespan. In this paper, we introduce a computational pipeline that uses a Large Language Model (LLM) to simulate diverse persona profiles and model these associations at scale. We constructed persona-event pairs by operationalising age, gender, nationality, origin environment, and personal values. Then we prompted the LLM to generate an odour description and its most likely source for each pair. A bottom-up open card sorting taxonomy is then applied to cluster the odour sources into 13 odour types, forming an exploratory olfactory memory map, while events are also clustered similarly into 33 event types. Empirical analyses confirm the significance of systematic odour-event associations being encoded in our LLM-generated sample. While interaction effects for lifespan were not significant, the total effects for events, lifespan and cultural indicators were significant. The interaction effects of our cultural indicators were also significant for our sample. This study is a step towards using LLMs as a computational proxy in the field of LLMs for personalised assistive technologies to be adaptive and culturally inclusive in order to aid patients with dementia.
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Odour-cued memory retrieval has emerged as a potential intervention for people with dementia due to the unique properties of odour-evoked memories. The design and application of technologies that integrate this strategy require knowing which odours are meaningful to a given person, motivating the systematic modelling of odour-event associations across the lifespan. In this paper, we introduce a computational pipeline that uses a Large Language Model (LLM) to simulate diverse persona profiles and model these associations at scale. We constructed persona-event pairs by operationalising age, gender, nationality, origin environment, and personal values. Then we prompted the LLM to generate an odour description and its most likely source for each pair. A bottom-up open card sorting taxonomy is then applied to cluster the odour sources into 13 odour types, forming an exploratory olfactory memory map, while events are also clustered similarly into 33 event types. Empirical analyses confirm the significance of systematic odour-event associations being encoded in our LLM-generated sample. While interaction effects for lifespan were not significant, the total effects for events, lifespan and cultural indicators were significant. The interaction effects of our cultural indicators were also significant for our sample. This study is a step towards using LLMs as a computational proxy in the field of LLMs for personalised assistive technologies to be adaptive and culturally inclusive in order to aid patients with dementia.
Automatic affect prediction systems usually assume its underlying affect representation scheme (ARS). This systematic review aims to explore how different ARS are used for in affect prediction systems based on spoken input. The focus is only on the audio input from speakers. Various datasets for speech emotion recognition were also involved in the study to understand the motivation for certain (categorical or dimensional) schemes used for emotions. The basis, popularity, advantages and target affective states were investigated. We used Scopus and Web of Science to extract the papers, focusing on the systems in the field of Computer Science in English language. In summary, our exploration of affect representation schemes in Speech Emotion Recognition (SER) reveals a predominant focus on categorical representations of affect, particularly variations of Ekman's six basic emotions. Behavior and attitude, although rare, are also represented sometimes. Emotions like anger, happiness, and sadness receive the most attention, while the recognition of the neutral state as an emotional state remains controversial. Dimensional affect representation schemes are less common, possibly due to the difficulty in estimating valence solely from audio input. Researchers often combine multiple categorical schemes to accommodate different datasets used in SER systems, aligning the popularity of the schemes with the corresponding datasets. However, issues such as a lack of explanation for chosen categories, interchangeable use of terminology, and a weak psychological foundation for category selection pose challenges in achieving a comprehensive understanding of affect representation in SER research.
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Automatic affect prediction systems usually assume its underlying affect representation scheme (ARS). This systematic review aims to explore how different ARS are used for in affect prediction systems based on spoken input. The focus is only on the audio input from speakers. Various datasets for speech emotion recognition were also involved in the study to understand the motivation for certain (categorical or dimensional) schemes used for emotions. The basis, popularity, advantages and target affective states were investigated. We used Scopus and Web of Science to extract the papers, focusing on the systems in the field of Computer Science in English language. In summary, our exploration of affect representation schemes in Speech Emotion Recognition (SER) reveals a predominant focus on categorical representations of affect, particularly variations of Ekman's six basic emotions. Behavior and attitude, although rare, are also represented sometimes. Emotions like anger, happiness, and sadness receive the most attention, while the recognition of the neutral state as an emotional state remains controversial. Dimensional affect representation schemes are less common, possibly due to the difficulty in estimating valence solely from audio input. Researchers often combine multiple categorical schemes to accommodate different datasets used in SER systems, aligning the popularity of the schemes with the corresponding datasets. However, issues such as a lack of explanation for chosen categories, interchangeable use of terminology, and a weak psychological foundation for category selection pose challenges in achieving a comprehensive understanding of affect representation in SER research.