  In this paper, we investigate the use of adversarial learning for
unsupervised adaptation to unseen recording conditions, more specifically,
single microphone far-field speech. We adapt neural networks based acoustic
models trained with close-talk clean speech to the new recording conditions
using untranscribed adaptation data. Our experimental results on Italian
SPEECON data set show that our proposed method achieves 19.8% relative word
error rate (WER) reduction compared to the unadapted models. Furthermore, this
adaptation method is beneficial even when performed on data from another
language (i.e. French) giving 12.6% relative WER reduction.

Audio and Speech Processing;Artificial Intelligence;Computation and Language;Sound