음성인식 기반 응급상황관제Emergency dispatching based on automatic speech recognition
- Other Titles
- Emergency dispatching based on automatic speech recognition
- Authors
- 이규환; 정지오; 신대진; 정민화; 강경희; 장윤희; 장경호
- Issue Date
- 2016
- Publisher
- 한국음성학회
- Keywords
- automatic speech recognition; emergency dispatching; word segmentation; semantic analysis
- Citation
- 말소리와 음성과학, v.8, no.2, pp.31 - 39
- Journal Title
- 말소리와 음성과학
- Volume
- 8
- Number
- 2
- Start Page
- 31
- End Page
- 39
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/9345
- DOI
- 10.13064/KSSS.2016.8.2.031
- ISSN
- 2005-8063
- Abstract
- In emergency dispatching at 119 Command & Dispatch Center, some inconsistencies between the ‘standard emergency aid system’ and ‘dispatch protocol,’ which are both mandatory to follow, cause inefficiency in the dispatcher’s performance. If an emergency dispatch system uses automatic speech recognition (ASR) to process the dispatcher’s protocol speech during the case registration, it instantly extracts and provides the required information specified in the 'standard emergency aid system,’ making the rescue command more efficient. For this purpose, we have developed a Korean large vocabulary continuous speech recognition system for 400,000 words to be used for the emergency dispatch system. The 400,000 words include vocabulary from news, SNS, blogs and emergency rescue domains. Acoustic model is constructed by using 1,300 hours of telephone call (8 kHz) speech, whereas language model is constructed by using 13 GB text corpus. From the transcribed corpus of 6,600 real telephone calls, call logs with emergency rescue command class and identified major symptom are extracted in connection with the rescue activity log and National Emergency Department Information System (NEDIS). ASR is applied to emergency dispatcher’s repetition utterances about the patient information. Based on the Levenshtein distance between the ASR result and the template information, the emergency patient information is extracted. Experimental results show that 9.15% Word Error Rate of the speech recognition performance and 95.8% of emergency response detection performance are obtained for the emergency dispatch system.
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