Deep Learning for Environmentally Robust Speech Recognition

Zhang, Zixing; Geiger, Jürgen T.; Pohjalainen, Jouni; Mousa, Amr El-Desoky; Jin, Wenyu; Schuller, Björn

doi:10.1145/3178115

Cited by 260 publications

(135 citation statements)

References 130 publications

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“…field of image processing [3][4][5][6][7]. Similar performances have been achieved in the field of 25 speech recognition [8,9] and natural language processing [10,11]. 26 A steadily growing amount of work has been exploring the application of deep 27 learning approaches on physiological signals.…”

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confidence: 84%

Exploring Deep Physiological Models for Nociceptive Pain Recognition

Thiam

Bellmann

Kestler³

et al. 2019

Preprint

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Standard feature engineering involves manually designing and assessing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features in order to optimize an inference model. Several studies have shown that this whole manual process can be efficiently replaced by deep learning approaches which are characterized by the integration of feature engineering, feature selection and inference model optimization into a single learning process. Such techniques have proven to be very successful in the domain of image processing and have been able to attain state-of-the-art performances while significantly outperforming traditional approaches based on hand-crafted features. In the following work, we explore deep learning approaches for the analysis of physiological signals. More precisely, deep learning architectures are designed for the assessment of measurable physiological channels in order to perform an accurate classification of different levels of artificially induced nociceptive pain. Most of the previous works related to pain intensity classification based on physiological signals rely on a carefully designed set of hand-crafted features in order to achieve a relatively good classification performance. Therefore, the current work aims at building competitive pain intensity classification models without the need of domain specific expert knowledge for the generation of relevant features. The assessment of the designed deep learning architectures is based on the BioVid Heat Pain Database (Part A) and experimental validation demonstrates that the proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outperform previous classification methods reported in the literature, with respective average performances of 85.03% and 83.76% for the binary classification experiment consisting of the discrimination between the baseline level and the pain tolerance level (T 0 vs.T 4 ) in a Leave-One-Subject-Out (LOSO) cross-validation evaluation setting.

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confidence: 84%

Exploring Deep Physiological Models for Nociceptive Pain Recognition

Thiam

Bellmann

Kestler³

et al. 2019

Preprint

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“…Deep learning is becoming popular, which has outperformed traditional methods in many fields, such as speech recognition [25] and face recognition [26]. In deep learning, CNN capable of strong self-learning is one of the most successful methods for image classification [27].…”

Section: D Cnnmentioning

confidence: 99%

NIRExpNet: Three-Stream 3D Convolutional Neural Network for Near Infrared Facial Expression Recognition

Chen

et al. 2017

Applied Sciences

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Facial expression recognition (FER) under active near-infrared (NIR) illumination has the advantages of illumination invariance. In this paper, we propose a three-stream 3D convolutional neural network, named as NIRExpNet for NIR FER. The 3D structure of NIRExpNet makes it possible to extract automatically, not just spatial features, but also, temporal features. The design of multiple streams of the NIRExpNet enables it to fuse local and global facial expression features. To avoid over-fitting, the NIRExpNet has a moderate size to suit the Oulu-CASIA NIR facial expression database that is a medium-size database. Experimental results show that the proposed NIRExpNet outperforms some previous state-of-art methods, such as Histogram of Oriented Gradient to 3D (HOG 3D), Local binary patterns from three orthogonal planes (LBP-TOP), deep temporal appearance-geometry network (DTAGN), and adapt 3D Convolutional Neural Networks (3D CNN DAP).

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“…The single-channel speech enhancement problem is to reduce a noise present in a single-channel recording of speech. This technique has many applications, it can be employed as a preprocessing step in speech or speaker recognition system [1] or to improve speech intelligibility what is important for example in hearing aids like cochlear implants [2].…”

Section: Introductionmentioning

confidence: 99%

Using Recurrences in Time and Frequency within U-net Architecture for Speech Enhancement

Grzywalski¹,

Drgas

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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When designing fully-convolutional neural network, there is a trade-off between receptive field size, number of parameters and spatial resolution of features in deeper layers of the network. In this work we present a novel network design based on combination of many convolutional and recurrent layers that solves these dilemmas. We compare our solution with Unets based models known from the literature and other baseline models on speech enhancement task. We test our solution on TIMIT speech utterances combined with noise segments extracted from NOISEX-92 database and show clear advantage of proposed solution in terms of SDR (signal-todistortion ratio), SIR (signal-to-interference ratio) and STOI (spectro-temporal objective intelligibility) metrics compared to the current state-of-the-art.

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Deep Learning for Environmentally Robust Speech Recognition

Cited by 260 publications

References 130 publications

Exploring Deep Physiological Models for Nociceptive Pain Recognition

Exploring Deep Physiological Models for Nociceptive Pain Recognition

NIRExpNet: Three-Stream 3D Convolutional Neural Network for Near Infrared Facial Expression Recognition

Using Recurrences in Time and Frequency within U-net Architecture for Speech Enhancement

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