인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.12
- 수록면
- 549 - 559 (11page)
- DOI
- 10.7232/JKIIE.2021.47.6.549
이용수
초록· 키워드
Recently, the demand for artificial intelligence-based voice services, identifying and appropriately responding to user needs based on voice, is increasing. In particular, technology for recognizing emotions, which is non-verbal information of human voice, is receiving significant attention to improve the quality of voice services. Therefore, speech emotion recognition models based on deep learning is actively studied with rich English data, and a multi-modal emotion recognition framework with a speech recognition module has been proposed to utilize both voice and text information. However, the framework with speech recognition module has a disadvantage in an actual environment where ambient noise exists. The performance of the framework decreases along with the decrease of the speech recognition rate. In addition, it is challenging to apply deep learning-based models to Korean emotion recognition because, unlike English, emotion data is not abundant. To address the drawback of the framework, we propose a consistency regularization learning methodology that can reflect the difference between the content of speech and the text extracted from the speech recognition module in the model. We also adapt pre-trained models with self-supervised way such as Wav2vec 2.0 and HanBERT to the framework, considering limited Korean emotion data. Our experimental results show that the framework with pre-trained models yields better performance than a model trained with only speech on Korean multi-modal emotion dataset. The proposed learning methodology can minimize the performance degradation with poor performing speech recognition modules.
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목차
- 1. 서론
- 2. 관련 연구
- 3. 제안 방법론
- 4. 실험 및 결과
- 5. 결론
- 참고문헌
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-ECN-0101-2022-530-000057328
