Acoustic synthesis of training data for speech recognition in living room environments

Despite continuous progress in robust automatic speech recognition acoustic mismatch between training and test conditions is still a major problem. Consequently, large speech collections must be conducted in many environments. An alternative approach is to generate training data synthetically by filtering clean speech with impulse responses and/or adding noise signals from the target domain. We compare the performance of a speech recognizer trained on recorded speech in the target domain with a system trained on suitably transformed clean speech. In order to obtain comparable results, our experiments are based on two channel recordings with a close talk and a distant microphone which produce the clean signal and the target domain signal respectively. By filtering and adding noise we obtain error rates which are only 10% higher for natural number recognition and 30% higher for a command recognition task compared to training with target domain data.