Advancing Spoken Dialog Systems for Manufacturing: From Conceptual Architecture and Taxonomy to Real Case Applications and Future Directions (2023)

Abstract: This research encompasses a comprehensive exploration of Spoken Dialogue Systems (SDSs) in the manufacturing sector. It begins by establishing a conceptual architecture and taxonomy to guide the design and selection of SDS elements. Real case applications, including worker safety and cybersecurity support, validate the research findings and highlight areas for improvement. Looking ahead, the study delves into the potential of Large Language Models (LLMs) and multi-modal applications. Emphasizing the importance of extreme personalization, the study highlights the need to cater to the diverse qualifications and preferences of workers. Additionally, it investigates the integration of SDSs with other sensory modalities, such as images, videos, and augmented or virtual reality scenarios, to enhance the user experience and productivity. The research also addresses crucial considerations related to knowledge base optimization. It examines semantic variations of words across different application contexts, the continuous updating of procedures and data, and the adaptability of SDSs to diverse dialects and linguistic abilities, particularly in low-schooling personnel scenarios. Privacy, industrial protection, and ethical concerns in the era of LLMs and external players like OpenAI are given due attention. The study explores the boundaries of knowledge that conversational systems should possess, advocating for transparency, explainability, and responsible data handling practices.

## future of spoken dialog research i think the future of sdss research is in extreme personalization. if we think of an sds to support workers their different qualification has to be considered. a balance must be maintained between conciseness and ease of understanding. the way an experienced user and a novice approach the system can vary, as the novice lacks sufficient knowledge or experience. additionally, other factors such as specific situations (like emergencies) may also play a role. in the future also the use of llms and related privacy issues should be considered. the use of llm and external players such as openai frightens the industry. for an optimal kb, it is necessary to provide reports, data, and organizational values for greater customization. such data sharing with an external player needs to be evaluated in terms of privacy and industry protection. the third aspect is the evolution of sdss. what do we expect in the future? how do we envision the integration of these systems with other senses such as sight? how to take into account the need for explainable and interpretable ai? what conversational systems should and should not know. what are the limits of knowledge? who is holding it? how to empower industries with deep knowledge of the model.