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. 2022;8(5):3899-3917.
doi: 10.1007/s40747-022-00664-2. Epub 2022 Mar 30.

Counseling (ro)bot as a use case for 5G/6G

Yoshio Taniguchi  1 , Yukino Ikegami  2 , Hiroshi Fujikawa  3 , Yogesh Pathare  4 , Andrea Kutics  5 , Banzi Massimo  6 , Marco Anisetti  7 , Ernesto Damiani  7 , Yoshitaka Sakurai  8 , Setsuo Tsuruta  8
Affiliations

Counseling (ro)bot as a use case for 5G/6G

Yoshio Taniguchi et al. Complex Intell Systems. 2022.

Abstract

This paper presents a counseling (ro)bot called Visual Counseling Agent (VICA) which focuses on remote mental healthcare. It is an agent system leveraging artificial intelligence (AI) to aid mentally distressed persons through speech conversation. The system terminals are connected to servers by the Internet exploiting Cloud-nativeness, so that anyone who has any type of terminal can use it from anywhere. Despite a promising voice communication interface, VICA shows limitations in conversation continuity on conventional 4G networks. Concretely, the use of the current 4G networks produces word dropping, delayed response, and the occasional connection failure. The objective of this paper is to mitigate these issues by leveraging a 5G/6G slice inclusive of mobile/multiple edge computing (MEC). First, we propose and partly implement the enhanced and advanced version of VICA. Servers of enhanced versions collaborate to increase speech recognition reliability. Although it significantly increases generated data volume, the advanced version enables a recognition of the facial expressions to greatly enhance counseling quality. Then, we propose a quality assurance mechanism using multiple levels of catalog, as well as 5G/6G slice inclusive of MEC, and conduct experiments to uncover issues related to the 4G. Results indicate that the number of speech recognition errors in Internet Cloud is more than twofold compared to edge computing, implying that quality assurance using 5G/6G in conjunction with VICA Counseling (ro)bot has higher efficiency.

Keywords: 5G network; Artificial intelligence; Human–computer interaction (HCI); Natural language processing.

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Conflict of interest statement

Conflict of interestThere is no conflict of interest.

Figures

Fig. 1
Fig. 1
CRECA architecture. Mecab Morphological Analyzer (Japanese tokenizer), and ML-ASK System for Affect Analysis of Textual Input in Japanese
Fig. 2
Fig. 2
Module structure for VICA’s processing
Fig. 3
Fig. 3
Network configuration for solving cloud-native speech recognition problems. ORQ/MRQ/HRQ/VHRQ: Ordinary/Middle/High/Very High Requirement Quality
Fig. 4
Fig. 4
Enhanced VICA network configuration for improving speech recognition cloud
Fig. 5
Fig. 5
System architecture of enhanced VICA
Fig. 6
Fig. 6
Network configuration of enhanced VICA for speech recognition reliability improvement and its evaluation
Fig. 7
Fig. 7
Network configuration of advanced VICA and AR server for remote human counselor
Fig. 8
Fig. 8
VICA advanced version system architecture
See this image and copyright information in PMC

References

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