We present a two-stage noise spectra estimation approach. After the first-stage noise estimation using the improved minima controlled recursive averaging (IMCRA) method, the second-stage noise estimation is performed by employing a maximum a posteriori (MAP) noise amplitude estimator. We also develop a regression-based speech enhancement system by approximating the clean speech with the estimated noise and the original noisy speech. Evaluation experiments show that the proposed two-stage noise estimation method results in lower estimation error for all test noise types. Compared to the original noisy speech, the proposed regression-based approach obtains an average relative word error rate (WER) reduction of 65% in our isolated word recognition experiments conducted in 12 real car environments.
['Weifeng Li', 'Katunobu Itou', 'Kazuya Takeda', 'Fumitada Itakura']
Two-stage noise spectra estimation and regression based in-car speech recognition using single distant microphone