Feature recognition of spoken Japanese input based on support vector machine

The feature recognition of spoken Japanese is an effective carrier for Sino-Japanese communication. At present, most of the existing intelligent translation equipment only have equipment that converts English into other languages, and some Japanese translation systems have problems with accuracy and real-time translation. Based on this, based on support vector machines, this research studies and recognizes the input features of spoken Japanese, and improves traditional algorithms to adapt to the needs of spoken language recognition. Moreover, this study uses improved spectral subtraction based on spectral entropy for enhancement processing, modifies Mel filter bank, and introduces several improved MFCC feature parameters. In addition, this study selects an improved feature recognition algorithm suitable for this research system and conducts experimental analysis of input feature recognition of spoken Japanese on the basis of this research model. The research results show that this research model has improved the recognition speed and recognition accuracy, and this research model meets the system requirements, which can provide a reference for subsequent related research.