음성 데이터 전처리 기법에 따른 뉴로모픽 아키텍처 기반 음성 인식 모델의 성능 분석
Performance Analysis of Speech Recognition Model based on Neuromorphic Architecture of Speech Data Preprocessing Technique
- Resource Type
- Article
Text
- Authors
- 조진성; 김봉재; Jinsung Cho; Bongjae Kim
- Source
- 한국인터넷방송통신학회 논문지, 06/30/2022, Vol. 22, Issue 3, p. 69-74
- Subject
- Artificial Intelligence
Deep Learning
Neuromorphic Computing
Speech Recognition
- Language
- 한국어(KOR)
- ISSN
- 2289-0238
SNN (Spiking Neural Network) operating in neuromorphic architecture was created by mimicking human neural networks. Neuromorphic computing based on neuromorphic architecture requires relatively lower power than typical deep learning techniques based on GPUs. For this reason, research to support various artificial intelligence models using neuromorphic architecture is actively taking place. This paper conducted a performance analysis of the speech recognition model based on neuromorphic architecture according to the speech data preprocessing technique. As a result of the experiment, it showed up to 84% of speech recognition accuracy performance when preprocessing speech data using the Fourier transform. Therefore, it was confirmed that the speech recognition service based on the neuromorphic architecture can be effectively utilized.