Adaptive regression based framework for in-car speech recognition

We address issues for improving hands-free speech recognition performance in different car environments using a single distant microphone. In our previous work, we proposed a regression based enhancement method for in-car speech recognition. In this paper, we describe recent improvements and propose a data-driven adaptive regression based speech recognition system, in which both feature enhancement and model compensation are performed. Based on isolated word recognition experiments conducted in 15 real car environments, the proposed adaptive regression approach shows an advantage in average relative word error rate (WER) reductions of 52.5% and 14.8%, compared to original noisy speech and ETSI advanced front-end, respectively.