Intelligent Assessment Method of Communication Interference Speech Quality Based on End-to-end Network

Speech quality can reflect the interference in the environment during speech communications. This paper focuses on evaluating speech quality in communication interference environments, and introduces an innovative end-to-end network-based intelligent evaluation method. Utilizing a transformer network structure, the method involves segmenting interference speech into time frames, extracting Mel and amplitude spectrograms, and constructing feature maps for deep feature extraction and quality assessment. Tested on a communication interference speech dataset, this end-to-end approach achieved a remarkable 93% accuracy in evaluating interference speech quality, outperforming CNN-based methods by 5.5%. This significantly enhances the precision of assessing interference speech quality.