Analysis of CFA-BF: Novel combined fixed/adaptive beamforming for robust speech recognition in real car environments

Among a number of studies which have investigated various speech enhancement and processing schemes for in-vehicle speech systems, the delay-and-sum beamforming (DASB) and adaptive beamforming are two typical methods that both have their advantages and disadvantages. In this paper, we propose a novel combined fixed/adaptive beamforming solution (CFA-BF) based on previous work for speech enhancement and recognition in real moving car environments, which seeks to take advantage of both methods. The working scheme of CFA-BF consists of two steps: source location calibration and target signal enhancement. The first step is to pre-record the transfer functions between the speaker and microphone array from different potential source positions using adaptive beamforming under quiet environments; and the second step is to use this pre-recorded information to enhance the desired speech when the car is running on the road. An evaluation using extensive actual car speech data from the CU-Move Corpus shows that the method can decrease WER for speech recognition by up to 30% over a single channel scenario and improve speech quality via the SEGSNR measure by up to 1 dB on the average.