To address the limitations of the fixed beamformer in the generalized sidelobe canceller (GSC) in suppressing sidelobe interference and processing non-stationary speech signals in complex environments, this paper proposes an improved GSC-based speech enhancement method utilizing a parameterized beamformer.The proposed method employs a dynamic tuning mechanism to flexibly balance and adjust between the delay-and-sum (DS) beamformer and the super-directive (SD) beamformer, effectively suppressing sidelobe interference and enhancing the robustness and adaptability of the GSC in complex acoustic environments. Furthermore, a cross-correlation coefficient is introduced to regulate the step size of the adaptive filter weight update, mitigating the over-attenuation issue caused by variations in speech signals and improving the processing accuracy for non-stationary speech signals. Simulation experiments were conducted in MATLAB to evaluate the performance of the proposed method under various noise conditions, including Babble noise, music noise, and white noise. The performance of the proposed method was compared with that of the traditional GSC and the GSC with the least mean square (LMS) algorithm. The evaluation was carried out from multiple perspectives, including 3D beam patterns, noise reduction effects under different background noise and parameter conditions, and the effectiveness of the cross-correlation coefficient. Quantitative analysis was performed using performance metrics such as segmental signal-to-noise ratio (SNR) and perceptual evaluation of speech quality (PESQ).The results demonstrate that the proposed method significantly outperforms the traditional GSC in terms of noise reduction performance and speech clarity. In environments with Babble noise, music noise, and white noise, the segmental SNR improved to 11.02, 6.14, and 10.33 dB, respectively, while the PESQ values increased to 3.65, 3.20, and 3.25, respectively. By adjusting the parameters, the proposed method achieves optimal noise reduction effects in different noise environments, validating its effectiveness and superiority in complex acoustic scenarios.