Anti Noise Speech Recognition Based on Deep Learning in Wireless Communication Networks

Anti Noise Speech Recognition Based on Deep Learning in Wireless Communication Networks

연구 분야: Networking

학회: International Conference on Advanced Hybrid Information Processing

As a new high-tech industry, the application of speech recognition technology is becoming more and more competitive, with a wide range of application fields and application prospects, and has far-reaching significance for the development of science and technology. The communication environment of wireless communication network will bring various types of noise to speech, so an anti noise speech recognition method based on deep learning of wireless communication network is designed to achieve anti noise speech recognition in this environment. The voice signal of wireless communication network is preprocessed by anti aliasing filtering, analog-to-digital conversion, pre emphasis, framing and windowing, endpoint detection, etc. A series of denoising processes are implemented for the voice signal of wireless communication network, and different speech preprocessing methods are adopted for different characteristics of noise. A speech signal feature extraction method based on improved EMD is designed and implemented. The speech recognition model is designed based on the regression neural network in deep learning, and the anti noise speech recognition of wireless communication network is realized. Test results show that the lowest word error rate of this method is 0.156, and the word error rate is also low.