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Paper Abstract and Keywords
Presentation 2024-02-20 11:35
[Special Talk] Prediction of Event Locations from Urgent Call Using Speech Recognition and Generative AI
Masaki Yoshida, Keisuke Maeda, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido University)
Abstract (in Japanese) (See Japanese page) 
(in English) Operators handling road-related urgent calls are required to pinpoint the event location from information verbally communicated by the reporter. This task demands geographical knowledge of the managed area and operational experience. Therefore, constructing a method that can predict the event location from the call would improve operational efficiency. In this paper, we focus on predicting the event location by first employing speech recognition AI to convert urgent calls into text and then using text generation AI to extract information relevant to the event location. This process facilitates location prediction on map applications.
Keyword (in Japanese) (See Japanese page) 
(in English) Urgent call / Geometrical location prediction / Speech recognition / Text generation / / / /  
Reference Info. ITE Tech. Rep., vol. 48, no. 6, ME2024-42, pp. 128-131, Feb. 2024.
Paper # ME2024-42 
Date of Issue 2024-02-12 (MMS, ME, AIT) 
ISSN Online edition: ISSN 2424-1970
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Conference Information
Conference Date 2024-02-19 - 2024-02-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To ME 
Conference Code 2024-02-ITS-IE-ME-AIT-MMS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Prediction of Event Locations from Urgent Call Using Speech Recognition and Generative AI 
Sub Title (in English)  
Keyword(1) Urgent call  
Keyword(2) Geometrical location prediction  
Keyword(3) Speech recognition  
Keyword(4) Text generation  
1st Author's Name Masaki Yoshida  
1st Author's Affiliation Hokkaido University (Hokkaido University)
2nd Author's Name Keisuke Maeda  
2nd Author's Affiliation Hokkaido University (Hokkaido University)
3rd Author's Name Ren Togo  
3rd Author's Affiliation Hokkaido University (Hokkaido University)
4th Author's Name Takahiro Ogawa  
4th Author's Affiliation Hokkaido University (Hokkaido University)
5th Author's Name Miki Haseyama  
5th Author's Affiliation Hokkaido University (Hokkaido University)
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Speaker Author-1 
Date Time 2024-02-20 11:35:00 
Presentation Time 10 minutes 
Registration for ME 
Paper # MMS2024-26, ME2024-42, AIT2024-26 
Volume (vol) vol.48 
Number (no) no.6 
Page pp.128-131 
Date of Issue 2024-02-12 (MMS, ME, AIT) 

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