CSA-BF: novel constrained switched adaptive beamforming for speech enhancement & recognition in real car environments

While a number of studies have investigated various speech enhancement and processing schemes for in-vehicle speech systems, little research has been performed using actual voice data collected in noisy car environments. We propose a new constrained switched adaptive beamforming algorithm (CSA-BF) for speech enhancement and recognition in real moving car environments. The proposed algorithm consists of a speech/noise constraint section, a speech adaptive beamformer, and a noise adaptive beamformer. We investigate CSA-BF performance with a comparison to classic delay-and-sum beamforming (DASB) in realistic car environments using a large quantity of data recorded in various car noise environments from across the United States. After analyzing the experimental results and considering the range of complex noise situations in the car environment using the CU-Move corpus, we formulate the CSA-BF algorithm. This method is shown to decrease WER (word error rate) for speech recognition by up to 31% and improve speech quality via the SEGSNR (segment signal-to-noise ratio) by up to 5.5 dB on the average, simultaneously.