## 2 Literature Review
Programming cobots by voice was reported by [46]. Several papers report vocal communication as part of a multimodal interface [47; 48; 49; 50]. An evaluation of the voice interface with Arduino-controlled robots is presented by [51]. A multimodal conversational agent for error-handling is presented in [52] and is mainly dominated by voice communication. Voice-Controlled Pick and Place tasks are described by [53]. [PERSON] et al. [54] showed that synchronizing speech command and gestures can reduce the errors and increase reliability. Recently, some studies have explored the potential of Natural Language Processing (NLP) for enhancing human-cobot interaction [55; 56]. With the advent of generative pretrained technology (GPT), a wave of studies heralded the potential of Large Language Models (LLMs) for improving HCC [57; 58; 59; 60; 61]. However, none of them showed real industrial application. Finally, a good tutorial on the use of LLMs for generating a cobot control code is described by [62] and recent surveys on the use of LLMs for interacting with robots are offered by [63; 64; 65]. Studies have also dealt with the social and psychological impact of cobots on society and workers. Psychological implications such as anxiety, trust, and the human-cobot relationship remain focal concerns [66; 67; 2]. A consistent theme across studies is the importance of trust and transparency in cobot decision-making to mitigate stress and resistance [66; 67; 68]. The emotional responses of workers to cobots are varied and range from developing attachments (dependency on the cobot) to experiencing competition with the cobots. These responses have brought the need for ethical frameworks for equitable implementation [69]. The perceived competency and usability of cobots also influence acceptance and collaboration quality [66]. Cobot integration is found to impact job satisfaction positively when workers feel safe and involved in decision-making processes [70]. Furthermore, cross-industry studies reveal the importance of cultural considerations in managing automation anxiety and preserving mental well-being [71]. Effective integration strategies, aligned with human-centric policies, are pivotal for maximizing benefits while minimizing disruption. To conclude, vocal communication methods, such as speech commands, free workers' visual and manual attention, allow multitasking and enhance productivity in dynamic environments. Recent advancements focus on voice programming for cobots, improving the reliability and precision of commands. Synchronized verbal commands with gestures further enhance interaction quality, reducing errors and increasing task efficiency. Research into vocal enhancements has introduced conversational agents that dominate error handling through speech-based systems, creating a natural and responsive collaborative experience. The integration of verbal interfaces in multimodal systems, combining voice with visual or tactile feedback, offers enriched communication while addressing complex tasks. Ongoing development emphasizes improving speech recognition accuracy, overcoming industrial noise challenges and fostering trust through transparent vocal feedback mechanisms. These advancements ensure verbal and vocal systems remain central to the future of seamless human-cobot interaction. Recent research highlights NLP and LLMs like GPT for improving human-cobot interaction, though industrial applications are limited. Studies emphasize psychological impacts, including trust, anxiety, and emotional responses like attachment or competition. Trust, usability, and cultural considerations are vital for reducing resistance and managing automation anxiety. Human-centric strategies ensure effective cobot integration and worker well-being.