Forensic speaker verification performance reduces significantly under high levels of noise and reverberation. Multiple channel speech enhancement algorithms, such as independent component analysis by entropy bound minimization (ICA-EBM), can be used to improve noisy forensic speaker verification performance. Although the ICA-EBM was used in previous studies to separate mixed speech signals under clean conditions, the effectiveness of using the ICA-EBM for improving forensic speaker verification performance under noisy and reverberant conditions has not been investigated yet. In this paper, the ICA-EBM algorithm is used to separate the clean speech from noisy speech signals. Features from the enhanced speech are obtained by combining the feature-warped mel frequency cepstral coefficients with similar features extracted from the discrete wavelet transform. The identity vector (i-vector) length normalized Gaussian probabilistic linear discriminant analysis is used as a classifier. The Australian Forensic Voice Comparison and QUT-NOISE corpora were used to evaluate forensic speaker verification performance under noisy and reverberant conditions. Simulation results demonstrate that forensic speaker verification performance based on ICA-EBM improves compared with that of the traditional independent component analysis under different types of noise and reverberation environments. For surveillance recordings corrupted with different types of noise (CAR, STREET and HOME) at − 10 dB signal to noise ratio, the average equal error rate of the proposed method based on ICA-EBM is better than that of the traditional ICA by 12.68% when the interview recordings are kept clean, and 7.25% when the interview recordings have simulated room reverberations.