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[PDF] Top 20 Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks

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Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks

Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks

... with speech impairment. Mengistu et al. [44] tackled these problems by using speaker adaptation technique like Maximum Likeli- hood Linear regression (MLLR) transformation followed by MAP adaptation ... See full document

101

Automatic speech recognition with deep neural networks for impaired speech

Automatic speech recognition with deep neural networks for impaired speech

... Automatic Speech Recognition has reached almost human performance in some controlled ...However, recognition of im- paired speech is a difficult task for two main reasons: data is (i) scarce ... See full document

11

Noisy training for deep neural networks in speech recognition

Noisy training for deep neural networks in speech recognition

... Although promising, the NN-based approach, either by the hybrid setting or the tandem setting, did not deliver overwhelming superiority over the conventional approaches based on MFCCs and GMMs. The revo- lution took ... See full document

14

New Paradigm in Speech Recognition: Deep Neural Networks

New Paradigm in Speech Recognition: Deep Neural Networks

... model using DNN A drawback of classical N-gram language models (LM) is their weak ability of generalization: if a sequence of words was not observed during training, N-gram model will give poor probability ... See full document

8

Dysarthric Speech Recognition Using a Convolutive Bottleneck Network

Dysarthric Speech Recognition Using a Convolutive Bottleneck Network

... In speech recognition technology, frame-wise features such as mel-frequency cepstral coefficients (MFCC), linear predic- tive coding (LPC), and an autoregressive model (AR) have been widely used so ...for ... See full document

5

Speech Emotion Recognition using Convolutional Neural Networks

Speech Emotion Recognition using Convolutional Neural Networks

... emotion recognition is inherently a multimodal process. Although speech modality conveys a large portion of the emotional information, it is not sufficient for recognizing affective states of humans in ... See full document

87

Continuous Speech Recognition by Neural Networks

Continuous Speech Recognition by Neural Networks

... on using NVIDIA GPUs to prop- erly work because the python library tensorflow-gpu which handles the Ten- sorFlow GPUs computations is built upon CUDA ...be using CPU for the experiments section as the main ... See full document

61

An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation

An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation

... in Table 2. It shows that SHL-MDNN achieves improvement over monolingual DNN baseline systems in all languages. Note that the use of multiple output layers in SHL-MDNN is similar to the concept of AO proposed for ... See full document

5

Multi-resolution speech analysis for automatic speech recognition using deep neural networks: Experiments on TIMIT

Multi-resolution speech analysis for automatic speech recognition using deep neural networks: Experiments on TIMIT

... the speech signal does not lose information, it implies selecting a par- ticular point in the trade-off between time and frequency ...the speech analysis. This means that with the typi- cal speech ... See full document

24

Joint Training Methods for Tandem and Hybrid Speech Recognition Systems using Deep Neural Networks

Joint Training Methods for Tandem and Hybrid Speech Recognition Systems using Deep Neural Networks

... read speech, or spontaneous speech with a natural speaking style from telephone conversations or broadcast ...Such speech data contain more variations in pronunciation and are harder to ...continuous ... See full document

220

Speech Recognition in noisy environment using Deep Learning Neural Network

Speech Recognition in noisy environment using Deep Learning Neural Network

... 2.3 Deep learning for speech recognition Developments in the field of deep neural networks demonstrated convincing improvements in speech recognition performance, ... See full document

118

Vehicle Brand Recognition by Deep Neural Networks

Vehicle Brand Recognition by Deep Neural Networks

... for handwriting recognition in ...on deep learning are mainly divided into two ...ROIs using the method of candidate ...convolution neural network for classification and ... See full document

6

Investigating Bilingual Deep Neural Networks for Automatic Speech Recognition of Code-switching Frisian Speech

Investigating Bilingual Deep Neural Networks for Automatic Speech Recognition of Code-switching Frisian Speech

... bilingual deep neural network (DNN)- based ASR system which is designed to recog- nize both Frisian and ...the recognition performance of both systems is compared in order to have a better ... See full document

9

Automatic dysfluency detection in dysarthric speech using deep belief networks

Automatic dysfluency detection in dysarthric speech using deep belief networks

... a speech disorder caused by difficulties in control- ling muscles, such as the tongue and lips, that are needed to produce ...cause speech to be slurred, mumbled, and spoken relatively slowly, and can also ... See full document

5

Speech dereverberation for enhancement and recognition using dynamic features constrained deep neural networks and feature adaptation

Speech dereverberation for enhancement and recognition using dynamic features constrained deep neural networks and feature adaptation

... 6.3.4 Effect of dynamic feature estimation We now investigate the effect of predicting dynamic log-magnitude spectra and using them to improve the static spectra. The results are shown in the rows DNN7, DNN7LS, ... See full document

18

Improving audio-visual speech recognition using deep neural networks with dynamic stream reliability estimates

Improving audio-visual speech recognition using deep neural networks with dynamic stream reliability estimates

... clean speech within the background noise has been done in a controlled manner to yield six different SNR conditions between -6 dB and 9 dB without rescaling the speech or noise ... See full document

6

Investigating Bilingual Deep Neural Networks for Automatic Recognition of Code-switching Frisian Speech

Investigating Bilingual Deep Neural Networks for Automatic Recognition of Code-switching Frisian Speech

... bilingual deep neural network (DNN)- based ASR system which is designed to recog- nize both Frisian and ...the recognition performance of both systems is compared in order to have a better ... See full document

8

HANDWRITING RECOGNITION USING NEURAL NETWORKS

HANDWRITING RECOGNITION USING NEURAL NETWORKS

... Character recognition is the process to classify the input character according to the predefine character ...artificial neural networks. In this paper Kohonen neural network is being ... See full document

9

Neural networks in recognition of handwriting

Neural networks in recognition of handwriting

... Artificial neural networks consist of many simple elements capable of processing ...created neural network in the process of handwriting ...out using the same set of images (taken from ... See full document

5

Offline signature recognition using neural networks approach

Offline signature recognition using neural networks approach

... either Offline or Online based on the ...made. Offline systems work on the scanned image of a ...for Offline Verification of signatures using a set of simple shape based geometric ...trained ... See full document

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