In order to enhance the reliability and accuracy of speaker recognition in courtrooms, and facilitate the transformation of scientific evaluation paradigm for courtroom voice analysis methods and processes, a novel method for automatic speaker recognition in courtrooms based on an improved emphasized channel attention propagation aggregation time delay neural network(ECAPA-TDNN) architecture was proposed. This method integrates spatial attention mechanism, channel attention mechanism, and multi-head attention mechanism to enhance the accuracy and generalization capability of the model. The network model utilized a fusion of spectrogram and gammatone frequency cepstral coefficients(GFCC), selecting the one with the best training performance as the input. The trained neural network was employed as a deep feature extractor, followed by evaluating the strength of speech evidence using a likelihood ratio quantification evaluation system specifically designed for courtroom evidence. Experimental results demonstrate that on the VoxCeleb1 dataset, the C_llr value is 0.156, outperforming the previously published literature on automatic speaker recognition systems in courtrooms. On the Chinese zhaishell dataset, the false acceptance rate and false rejection rate both reach zero, with a minimum likelihood ratio supporting the homogeneity hypothesis of 1.72 × 10⁶ and a maximum likelihood ratio supporting the heterogeneity hypothesis of 5.83 × 10 ^-21. Consequently, this method further enhances the reliability and accuracy of the recognition system, providing robust support for the conclusion of evaluating speech evidence in courtrooms.