Total: 1
We address the speaker independent automatic recognition of spontaneous speech in highly instationary noise by applying semi-supervised sparse non-negative matrix factorization (NMF) for speech enhancement coupled with our recently proposed front end utilizing bottleneck (BN) features generated by a bidirectional Long Short-Term Memory (BLSTM) recurrent neural network. In our evaluation, we unite the noise corpus and evaluation protocol of the 2011 PASCAL CHiME challenge with the Buckeye database, and we demonstrate that the combination of NMF enhancement and BNBLSTM front end introduces significant and consistent gains in word accuracy in this highly challenging task at signal-to-noise ratios from -6 to 9 dB.