Speech Enhancement with LSTM Recurrent Neural Networks and its Application to Noise-Robust ASR

We evaluate some recent developments in recurrent neural network RNN based speech enhancement in the light of noise-robust automatic speech recognition ASR. The proposed framework is based on Long Short-Term Memory LSTM RNNs which are discriminatively trained according to an optimal speech reconstruction objective. We demonstrate that LSTM speech enhancement, even when used 'naïvely' as front-end processing, delivers competitive results on the CHiME-2 speech recognition task. Furthermore, simple, feature-level fusion based extensions to the framework are proposed to improve the integration with the ASR back-end. These yield a best result of 13.76﾿% average word error rate, which is, to our knowledge, the best score to date.