In ASR (Automatic Speech Recognition) applications, one of the most important issues in the real-time beamforming of microphone arrays is the inability to capture the whole acoustic dynamics via a finite-length of data and a finite number of array elements. For example, the reflected source signal impinging from the side-lobe direction presents a coherent interference, and the non-minimal phase channel dynamics may require an infinite amount of data in order to achieve perfect equalization (or inversion). All these factors appear as uncertainties or un-modeled dynamics in the receiving signals. Traditional adaptive algorithms such as NLMS that do not consider these errors will result in performance deterioration. In this paper, a time domain beamformer using H∞ filtering approach is proposed to adjust the beamforming parameters. Furthermore, this work also proposes a frequency domain approach called SPFDBB (Soft Penalty Frequency Domain Block Beamformer) using H∞ filtering approach that can reduce computational efforts and provide a purified data to the ASR application. Experimental results show that the adaptive H∞ filtering method is robust to the modeling errors and suppresses much more noise interference than that in the NLMS based method. Consequently, the correct rate of ASR is also enhanced.