Combining Bottleneck-BLSTM and Semi-Supervised Sparse NMF for Recognition of Conversational Speech in Highly Instationary Noise
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چکیده
We address the speaker independent automatic recognition of spontaneous speech in highly variable noise by applying semisupervised sparse non-negative matrix factorization (NMF) for speech enhancement coupled with our recently proposed frontend 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 BN-BLSTM 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.
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تاریخ انتشار 2012