Abstract
In life, the voice signals collected by people are essentially mixed signals, which mainly include information related to speaker characteristics, such as gender, age and emotional state. The commonality and characteristics of traditional single-dimensional speaker information recognition are analyzed, and children’s individualized analysis is carried out for common acoustic feature parameters such as prosodic features, sound quality features and spectral-based features. Therefore, considering the temporal characteristics of voice, combined with the Time-Delay Neural Network (TDNN) model, Bidirectional Long Short-Term Memory model and the attention mechanism, the multi-channel model is trained to form a speaker recognition problem solution for children’s speaker recognition. A large number of experimental results show that on the basis of guaranteeing the accuracy of age and gender recognition, higher accuracy of children’s voiceprint recognition can be obtained.
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This paper is funded by the “Dalian Key Laboratory for the Application of Big Data and Data Science”.
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Jia, N., Zheng, C., Sun, W. (2019). Children’s Speaker Recognition Method Based on Multi-dimensional Features. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_33
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DOI: https://doi.org/10.1007/978-3-030-35231-8_33
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