Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition

Abdel-Hamid, Ossama; Mohamed, Abdelrahman; Jiang, Hui; Penn, Gerald

doi:10.1109/icassp.2012.6288864

Cited by 681 publications

(211 citation statements)

References 7 publications

Supporting

Mentioning

208

Contrasting

Unclassified

Order By: Relevance

“…Subsampling can achieve invariance of features with regard to geometric distortion. Due to these advantages, CNN finds applications in computer vision [32][33][34], natural language processing [35,36], and speech recognition [37,38].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

et al. 2016

View full text Add to dashboard Cite

Near-infrared (NIR) face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN) for NIR face recognition (specifically face identification) in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA) NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.

show abstract

Section: Convolutional Neural Networkmentioning

confidence: 99%

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

et al. 2016

View full text Add to dashboard Cite

show abstract

“…DCNN has been widely used in the field of image recognition, speech recognition and computer vision fields [46][47][48], where it can achieve better performance than the traditional NN model. Recently, it has also been introduced to remote sensing data analysis, such as remote sensing scene classification [43][44][45] and object detection [49][50][51].…”

Section: Discussionmentioning

confidence: 99%

Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations

Hang

Liu

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Soil moisture (SM) plays an important role in hydrological cycle and weather forecasting. Satellite provides the only viable approach to regularly observe large-scale SM dynamics. Conventionally, SM is estimated from satellite observations based on the radiative transfer theory. Recent studies have demonstrated that the neural network (NN) method can retrieve SM with comparable accuracy as conventional methods. Here, we are interested in whether the NN model with more complex structures, namely deep convolutional neural network (DCNN), can bring about further improvement in SM retrievals when compared with the NN model used in recent studies. To achieve this objective, the same input data are used for the DCNN and NN models, including L-band Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB), C-band Advanced Scatterometer (ASCAT) backscattering coefficients, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and soil temperature. The target SM used to train the DCNN and NN models is the European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim) product. The experiment consists of two phases: the learning phase from 1 January to 31 December 2015 and the testing phase from 1 January to 31 December 2016. In the learning phase, we train the DCNN and NN models using the ERA-Interim SM. When evaluation between DCNN and NN against in situ measurements in the testing phase, we find that the temporal correlations between DCNN SM and in situ measurements are higher than those between NN SM and in situ measurements by 6.2% and 2.5% on ascending and descending orbits, respectively. In addition, from the perspective of temporal and spatial dynamics, the simulated SM values by DCNN/NN and the ERA-Interim SM agree relatively well at a global scale. Results suggest that both NN and DCNN models are effective in estimating SM from satellite observations, and DCNN can achieve slightly better performance than NN.

show abstract

“…Unfortunately, incorporating the delta features into the joint model presented here would be technically challenging, because we would have to propagate the error through the derivatives. However, training the network on several neighbouring spectro-temporal patches instead of just one is possible by modifying the proposed structure so that it has convolutional units (Abdel-Hamid et al, 2012;Vesely et al, 2011). This modification is highlighted in Fig.…”

Section: The Joint Optimization Of Spectro-temporal Features and Neurmentioning

confidence: 99%

Selection and enhancement of Gabor filters for automatic speech recognition

Kovács

Tóth

Compernolle

2014

Int J Speech Technol

View full text Add to dashboard Cite

Abstract. Motivated by neurophysiological studies, the use of Gabor filters as acoustic feature extractors for speech recognition purposes has received increasing attention in the new millenium. As the optimal parametrization of these filters is not obvious, many researchers employ different feature selection methods to find the best filter set. In this study, however, we argue that these kinds of feature selection methods cannot fulfill this task, as we demonstrate this with results obtained from experiments. We show that one can easily construct a better filter set manually, using simple heuristic rules. Then, as an alternative to the usual filter selection methods, we propose a training method that can jointly optimize the spectro-temporal filters and the neural net acoustic model built on them. In this special neural network achitecture, the filters are incorporated into the network and employed as the lowest layer of it. This allows us to tune the filters using backpropagation, and to manipulate them directly and not through their parameters. This method also has the advantage of reducing the task of filter set enhancement to that of a simple neural net training. Next, we show that we can enhance our manually selected filter set with this novel neural net architecture using the filter coefficients as initial values for the backpropagation training. The resulting filter sets were evaluated on the phone recognition task of the TIMIT corpus, using both clean and noise contaminated data; while cross-database phone recognition performance was evaluated on the "Szeged" Hungarian broadcast news database. The results we get demonstrate that the proposed filter optimization algorithm can outperform the usual feature selection-based methods, and that the filter set obtained by fine tuning the manual filters with the neural net algorithm performs even better, beating all the other methods in terms of performance.

show abstract

Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition

Cited by 681 publications

References 7 publications

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations

Selection and enhancement of Gabor filters for automatic speech recognition

Contact Info

Product

Resources

About