[PDF] Top 20 Unsupervised Speech Unit Discovery Using K-means and Neural Networks
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Unsupervised Speech Unit Discovery Using K-means and Neural Networks
... ], k-means are used on parameters generated by an auto-encoder (AE), also called Bottleneck Features (BnF), after ...binarization. k-means are similarly used in [ 14 ], with AEs and ... Voir le document complet
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Unsupervised Speech Unit Discovery Using K-means and Neural Networks
... ], k-means are used on parameters generated by an auto-encoder (AE), also called Bottleneck Features (BnF), after ...binarization. k-means are similarly used in [ 14 ], with AEs and ... Voir le document complet
14
Unsupervised and Lightly Supervised Part-of-Speech Tagging Using Recurrent Neural Networks
... avoid using such pre-processed and noisy alignments for label ...language and apply them in the tar- get ...Gouws and Søgaard, 2015a) and parallel corpora (T¨ackstr¨om et ...tagging ... Voir le document complet
11
Classification of Hate Speech Using Deep Neural Networks
... hate speech (Mohaouchane et ...(GBDT) and Logistic Regression (Badjatiya et ...Convolutional Neural Network (CNN) captures the local patterns in the text (Kim, ...Recurrent Unit (GRU) model ... Voir le document complet
12
Acoustic models for speech recognition using Deep Neural Networks based on approximate math
... Deep Neural Networks ...the speech recognition community. For Automatic Speech Recognition (ASR), DNN-based models result in 10-30% relative improvement in word error rates over traditional ... Voir le document complet
83
Machine tool volumetric error features extraction and classification using principal component analysis and K-means
... analysis and pattern ...or neural networks based clustering, ...the K-means algorithm can not only be simply implemented in solving many practical problems but also can be applied ... Voir le document complet
16
Structured prediction and generative modeling using neural networks
... utilize neural networks to effectively model data with sequen- tial ...order and the structure of the information is ncredibly ...data and machine ...losses, and optimization choices in ... Voir le document complet
107
Unsupervised Hyperspectral Band Selection Using Clustering and Single-layer Neural Network
... 1) k-Means clustering —following a bisecting k-Means fa- shion (Banerjee et ...; and 2) single-layer neu- ral networks (SLNN) (Haykin, ...by k-Means into two ... Voir le document complet
11
Speech synthesis using recurrent neural networks
... ture and hyperparameters, as well as limited compute power at our disposal, we made our own design choices so that the model would fit on a single GPU while having a receptive field of around 250 milliseconds ... Voir le document complet
74
Preliminary Experiments on Unsupervised Word Discovery in Mboshi
... states and the number of types are automatically adjusted based on the available ...(states) and one for controling the number of words; as in [14], the base distribution is also a hierarchical PYP language ... Voir le document complet
6
Using neural networks to describe tracer correlations
... The neural network used to produce the CH4 -N 2 O correlation in Panel (a) used Quickprop learning and one hidden layer with eight ...solution and the neural network solution was ...the ... Voir le document complet
5
Low-activity supervised convolutional spiking neural networks applied to speech commands recognition
... Deep Neural Networks (DNNs) are the current state- of-the-art models in many speech related ...friendly and energy efficient models, named Spiking Neural Networks ...manner, ... Voir le document complet
8
Approximating optimal state feedback using neural networks
... Figure 7: Data in the Center of Arcs Contributes Little to Training Symptoms of these trajectory based training examples were found both close to the origin, where [r] ... Voir le document complet
24
Robust Unsupervised Audio-visual Speech Enhancement Using a Mixture of Variational Autoencoders
... n and ν f n = 0, ∀(f, ...AV-VAE, and the proposed VAE- ...frames and adding to the associated lips im- ages random patches of standard Gaussian ...standard speech enhancement scores, ...[19] ... Voir le document complet
6
Adaptive motor control using predictive neural networks
... The claims made in this thesis rely mainly on empirical results obtained from simulations, and these claims need to be substantiated in tests on other dynamical s[r] ... Voir le document complet
102
NMR metabolic analysis of samples using fuzzy K-means clustering
... 4. K-means clustering of (A) 1 H NMR spectral profiles of metabolites for the replicates of five cell lines clustered into five clusters and (B) 1 H NMR spectral profiles of metabolites for the urine ... Voir le document complet
10
De-noising and de-blurring of images using deep neural networks
... In this paper, we test using a Learning Synthesis Deep Neural Network (LS-DNN) [2] in combination with BM3D [3], an off the shelf de-noising tool, to generate images, att[r] ... Voir le document complet
12
Image and video text recognition using convolutional neural networks
... CONVOLUTIONAL NEURAL NETWORKS 44 topology of six ...frequencies and 6 orientations, with σ = ...subsampling and convolution layers as in the ...Convolutional networks, reaching better ... Voir le document complet
178
Capturing and Explaining Trajectory Singularities using Composite Signal Neural Networks
... point and the location of several checkpoints to find in a set ...disappeared and players had to navigate their way to the ...handedness and self-assessment of spatial ...age and gender ... Voir le document complet
6
Approximate Bayes Optimal Policy Search using Neural Networks
... updates and sampling techniques during the interac- tion, which may be too computationally expensive, even on very small MDPs (Castronovo et ...ficial Neural Networks for Bayesian Reinforcement ... Voir le document complet
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