Beschreibung: The modern approach to include long-term temporal context information into speech recognition systems is the use of recurrent neural networks, e.g.,\ bi-directional long short-term memory (BLSTM) networks. In this paper, we decouple the BLSTM from a preceding CNN-based feature extractor network allowing us to investigate the use of temporal context in both models in a modular fashion. Accordingly, we train the BLSTMs on posteriors, stemming from preceding CNNs which use various amounts of limited context in their input layer, and investigate to what extent the BLSTM is able to effectively make use of its long-term modeling capabilities. We show that it is beneficial to train the BLSTM on posteriors stemming from a temporal context-free acoustic model. Remarkably, the best performing combination of CNN acoustic model and BLSTM afterwards is a large-context CNN (expected), followed by a BLSTM which has been trained on context-free CNN output posteriors (surprising).

Beschreibung: Regression based on neural networks (NNs) has led to considerable advances in speech enhancement under non-stationary noise con-ditions. Nonetheless, speech distortions can be introduced when employing NNs trained to provide strong noise suppression. We propose to address this problem by first suppressing noise and sub-sequently restoring speech with specifically chosen NN topologies for each of these distinct tasks. A mask-estimating long short-term memory (LSTM) network is employed for noise suppression, while the speech restoration is performed by a fully convolutional encoder-decoder (CED) network, where we introduce temporal modeling capabilities by using a convolutional LSTM layer in the bottleneck. We show considerable performance gains over reference methods of up to 0.26 MOS points (PESQ) and the ability to significantly improve intelligibility in terms of STOI for low-SNR conditions.

Beschreibung: Estimating time-frequency domain masks for speech enhancement using deep learning approaches has recently become a popular field of research. In this paper, we propose a mask-based speech enhancement framework by using concatenated identical deep neural networks (CI-DNNs). The idea is that a single DNN is trained under multiple input and output signal-to-noise power ratio (SNR) conditions, using targets that provide a moderate SNR gain with respect to the input and therefore achieve a balance between speech component quality and noise suppression. We concatenate this single DNN several times without any retraining to provide enough noise attenuation. Simulation results show that our proposed CI-DNN outperforms enhancement methods using classical spectral weighting rules w. r. t. total speech quality and speech intelligibility. Moreover, our approach shows similar or even a little bit better performance with much fewer trainable parameters compared with a noisy-target single DNN approach of the same size. A comparison to the conventional clean-target single DNN approach shows that our proposed CI-DNN is better in speech component quality and much better in residual noise component quality. Most importantly, our new CI-DNN generalized best to an unseen noise type, if compared to the other tested deep learning approach.