In this paper, we introduce a newly-created corpus of whispered speech simultaneously recorded via a close-talking microphone and a non-audible murmur (NAM) microphone in both clean and noisy conditions. To benchmark the corpus, which has been freely released recently, experiments on automatic recognition of continuous whispered speech were conducted. When training and test conditions are matched, the NAM microphone is found to be more robust against background noise than the close-talking microphone. In mismatched conditions (noisy data, models trained on clean speech), we found that Vector Taylor Series (VTS) compensation is particularly effective for the NAM signal.
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