scholarly journals Investigation of full-sequence training of deep belief networks for speech recognition

2010 ◽  
Author(s):  
Abdel-rahman Mohamed ◽  
Dong Yu ◽  
L. Deng
Keyword(s):  
Speech Recognition ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Full Sequence

Noise Robust Speech Recognition Using Deep Belief Networks

2016 ◽  
Vol 15 (01) ◽  
pp. 1650005 ◽  
Author(s):  
Mahboubeh Farahat ◽  
Ramin Halavati
Keyword(s):  
Speech Recognition ◽  
Word Recognition ◽  
Markov Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Mel Frequency Cepstral Coefficients ◽  
Recognition Systems ◽  
Low Dimensional

Most current speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. In these systems acoustic inputs are represented by Mel Frequency Cepstral Coefficients temporal spectrogram known as frames. But MFCC is not robust to noise. Consequently, with different train and test conditions the accuracy of speech recognition systems decreases. On the other hand, using MFCCs of larger window of frames in GMMs needs more computational power. In this paper, Deep Belief Networks (DBNs) are used to extract discriminative information from larger window of frames. Nonlinear transformations lead to high-order and low-dimensional features which are robust to variation of input speech. Multiple speaker isolated word recognition tasks with 100 and 200 words in clean and noisy environments has been used to test this method. The experimental results indicate that this new method of feature encoding result in much better word recognition accuracy.

Influence of Primary Auditory Cortex in the Characterization of Autism Spectrum in Young Adults using Brain Connectivity Parameters and Deep Belief Networks: An fMRI Study

2020 ◽  
Vol 16 (9) ◽  
pp. 1059-1073
Author(s):  
Vidhusha Srinivasan ◽  
N. Udayakumar ◽  
Kavitha Anandan
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Language Processing ◽  
Brain Connectivity ◽  
Primary Auditory Cortex ◽  
Autism Spectrum ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
High Functioning ◽  
Low Functioning

Background: The spectrum of autism encompasses High Functioning Autism (HFA) and Low Functioning Autism (LFA). Brain mapping studies have revealed that autism individuals have overlaps in brain behavioural characteristics. Generally, high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, specific mechanisms associated with their functional capabilities are still under research. Objective: This work addresses the overlapping phenomenon present in autism spectrum through functional connectivity patterns along with brain connectivity parameters and distinguishes the classes using deep belief networks. Methods: The task-based functional Magnetic Resonance Images (fMRI) of both high and low functioning autistic groups were acquired from ABIDE database, for 58 low functioning against 43 high functioning individuals while they were involved in a defined language processing task. The language processing regions of the brain, along with Default Mode Network (DMN) have been considered for the analysis. The functional connectivity maps have been plotted through graph theory procedures. Brain connectivity parameters such as Granger Causality (GC) and Phase Slope Index (PSI) have been calculated for the individual groups. These parameters have been fed to Deep Belief Networks (DBN) to classify the subjects under consideration as either LFA or HFA. Results: Results showed increased functional connectivity in high functioning subjects. It was found that the additional interaction of the Primary Auditory Cortex lying in the temporal lobe, with other regions of interest complimented their enhanced connectivity. Results were validated using DBN measuring the classification accuracy of 85.85% for high functioning and 81.71% for the low functioning group. Conclusion: Since it is known that autism involves enhanced, but imbalanced components of intelligence, the reason behind the supremacy of high functioning group in language processing and region responsible for enhanced connectivity has been recognized. Therefore, this work that suggests the effect of Primary Auditory Cortex in characterizing the dominance of language processing in high functioning young adults seems to be highly significant in discriminating different groups in autism spectrum.

Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge

2017 ◽  
Vol 16 (2) ◽  
pp. 129-136 ◽  
Author(s):  
Tianming Zhan ◽  
Yi Chen ◽  
Xunning Hong ◽  
Zhenyu Lu ◽  
Yunjie Chen
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Brain Tumor Segmentation

Gait recognition based on model-based methods and deep belief networks

2016 ◽  
Vol 8 (3/4) ◽  
pp. 237 ◽  
Author(s):  
Mohamed Benouis ◽  
Mohamed Senouci ◽  
Redouane Tlemsani ◽  
Lotfi Mostefai
Keyword(s):  
Gait Recognition ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Model Based
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