https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

https://mediatum.ub.tum.de/doc/1137916/file.pdf

LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework

The recognition of spontaneous speech in highly variable noise is known to be a challenge, especially at low signal-to-noise ratios (SNR). In this paper, we investigate the effect of applying bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks for speech feature enhancement in noisy conditions. BLSTM networks tend to prevail over conventional neural network architectures, whenever the recognition or regression task relies on an intelligent exploitation of temporal context information. We show that BLSTM networks are well-suited for mapping from noisy to clean speech features and that the obtained recognition performance gain is partly complementary to improvements via additional techniques such as speech enhancement by non-negative matrix factorization and probabilistic feature generation by Bottleneck-BLSTM networks. Compared to simple multi-condition training or feature enhancement via standard recurrent neural networks, our BLSTM-based feature enhancement approach leads to remarkable gains in word accuracy in a highly challenging task of recognizing spontaneous speech at SNR levels between -6 and 9 dB.

Feature enhancement by bidirectional LSTM networks for conversational speech recognition in highly non-stationary noise

Highly spontaneous, conversational, and potentially emotional and noisy speech is known to be a challenge for today’s automatic speech recognition (ASR) systems, which highlights the need for advanced algorithms that improve speech features and models. Histogram Equalization is an efficient method to reduce the mismatch between clean and noisy conditions by normalizing all moments of the probability distribution of the feature vector components. In this article, we propose to combine histogram equalization and multi-condition training for robust keyword detection in noisy speech. To better cope with conversational speaking styles, we show how contextual information can be effectively exploited in a multi-stream ASR framework that dynamically models context-sensitive phoneme estimates generated by a long short-term memory neural network. The proposed techniques are evaluated on the SEMAINE database—a corpus containing emotionally colored conversations with a cognitive system for “Sensitive Artificial Listening”.

Multi-stream LSTM-HMM decoding and histogram equalization for noise robust keyword spotting

Noise robust ASR in reverberated multisource environments applying convolutive NMF and Long Short-Term Memory

Lane-keeping assistance systems for vehicles may be more acceptable to users if the assistance was adaptive to the driver’s state. To adapt systems in this way, a method for detection of driver distraction is needed. Thus, we propose a novel technique for on-line detection of driver’s distraction, modeling the long-range temporal context of driving and head tracking data. We show that Long Short-Term Memory (LSTM) recurrent neural networks enable a reliable, subject-independent detection of inattention with an accuracy of up to 96.6 %. Thereby our LSTM framework significantly outperforms conventional approaches such as Support Vector Machines.

On-line Driver Distraction Detection using Long Short-Term Memory

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On-line Driver Distraction Detection using Long Short-Term Memory