An Exploration of Mimic Architectures for Residual Network Based Spectral Mapping

Spectral mapping uses a deep neural network (DNN) to map directly from noisy speech to clean speech. Our previous study found that the performance of spectral mapping improves greatly when using helpful cues from an acoustic model trained on clean speech. The mapper network learns to mimic the input favored by the spectral classifier and cleans the features accordingly. In this study, we explore two new innovations: we replace a DNN-based spectral mapper with a residual network that is more attuned to the goal of predicting clean speech. We also examine how integrating long term context in the mimic criterion (via wide-residual biLSTM networks) affects the performance of spectral mapping compared to DNNs. Our goal is to derive a model that can be used as a preprocessor for any recognition system; the features derived from our model are passed through the standard Kaldi ASR pipeline and achieve a WER of 9.3%, which is the lowest recorded word error rate for CHiME-2 dataset using only feature adaptation.Comment: Published in the IEEE 2018 Workshop on Spoken Language Technology (SLT 2018

Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling

Monaural speech enhancement has made dramatic advances since the introduction of deep learning a few years ago. Although enhanced speech has been demonstrated to have better intelligibility and quality for human listeners, feeding it directly to automatic speech recognition (ASR) systems trained with noisy speech has not produced expected improvements in ASR performance. The lack of an enhancement benefit on recognition, or the gap between monaural speech enhancement and recognition, is often attributed to speech distortions introduced in the enhancement process. In this study, we analyze the distortion problem, compare different acoustic models, and investigate a distortion-independent training scheme for monaural speech recognition. Experimental results suggest that distortion-independent acoustic modeling is able to overcome the distortion problem. Such an acoustic model can also work with speech enhancement models different from the one used during training. Moreover, the models investigated in this paper outperform the previous best system on the CHiME-2 corpus