UNSUPERVISED ADAPTATION OF A DENOISING AUTOENCODER BY BAYESIAN FEATURE ENHANCEMENT FOR REVERBERANT ASR UNDER MISMATCH CONDITIONS

The parametric Bayesian Feature Enhancement (BFE) and a data-driven Denoising Autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different conditions by an appropriate parameter set-ting, while the latter needs to be trained on conditions similar to the ones expected at decoding time, making it vulnerable to a mismatch between training and test conditions. We use a DNN backend and study reverberant ASR under three types of mismatch conditions: different room reverberation times, different speaker to microphone distances and the difference between artificially reverberated data and the recordings in a reverberant environment. We show that for these mismatch conditions BFE can provide the targets for a DA. This unsupervised adaptation provides a performance gain over the direct use of BFE and even enables to compensate for the mismatch of real and simulated reverberant data. Index Terms — robust speech recognition, deep neuronal net-works, feature enhancement, denoising autoencoder 1