Experiments of In-Car Audio Compensation for Hands Free Speech Recognition

The work presented in this paper aims at improving in-car speech recognition performance in presence of interfering signals diffused by the loudspeakers, addressing the well known problem of acoustic echo cancellation. A NLMS-based technique has been tested with real in-car speech recordings and issues like algorithm`s parameters and convergence or test/training mismatch have been investigated. The echo canceller shows good properties as acceptable adaptation speed and negligible speech distortion: in the best configuration the WER was lowered from 18.3 % to 5.5 %. Beneficial effects have also been observed by adopting a contamination technique in the acoustic model training phase