[PDF] Top 20 Speech Emotion Recognition: Recurrent Neural Networks compared to SVM and Linear Regression
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Speech Emotion Recognition: Recurrent Neural Networks compared to SVM and Linear Regression
... Kerkeni and al. [2] and modulation spectral features (MSFs) ...the recognition rate for each combination of various features and classifiers for Berlin and Spanish ...MFCC and MS ... Voir le document complet
3
Emotion Recognition with Deep Neural Networks
... important to explore joint learning of features from different ...with recurrent networks (Chen and Jin, 2015; He et ...methods to deal with challenges arising from different temporal ... Voir le document complet
145
Classification of Hate Speech Using Deep Neural Networks
... shown to be very powerful in classifying hate speech (Mohaouchane et ...(GBDT) and Logistic Regression (Badjatiya et ...Convolutional Neural Network (CNN) captures the local patterns in ... Voir le document complet
12
Speech Communication Automatic speech emotion recognition using an optimal combination of features based on EMD-TKEO
... for speech emotion recognition (SER) system using empirical mode decomposition ...envelope and instantaneous frequency of a signal that is supposed to be Amplitude Modulation-Frequency ... Voir le document complet
31
Time-continuous Estimation of Emotion in Music with Recurrent Neural Networks
... Music Emotion Recognition still is a hot topic in Music In- formation ...challenging and very interesting scientific task: 1) ambiguity and granularity of emotion de- scription, 2) ... Voir le document complet
5
Sous-continents Estimation of Emotion in Music with Recurrent Neural Networks
... Music Emotion Recognition still is a hot topic in Music In- formation ...challenging and very interesting scientific task: 1) ambiguity and granularity of emotion de- scription, 2) ... Voir le document complet
4
Unsupervised and Lightly Supervised Part-of-Speech Tagging Using Recurrent Neural Networks
... Results and discussion ...performance compared to those with two hidden lay- ...640 and 160 neurons ...close to that of the simple pro- jection tagger, the difference coming mostly from ... Voir le document complet
11
Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks
... dependencies and high-level structure in the data. Recurrent neural networks (RNN) (Rumelhart et ...suited to model temporal sequences, such as frames in a magnitude spectrogram or fea- ... Voir le document complet
159
A Hierarchical Classification of First-Order Recurrent Neural Networks
... of neural computability from the point of view infinite word reading automata ...automata-theoretic to the neural network context, and a transfinite decidable hierarchical classi- fication of ... Voir le document complet
13
Is it time to switch to Word Embedding and Recurrent Neural Networks for Spoken Language Understanding?
... SLU: SVM [1], HVS [2], Machine translation models, Finite State Transducers and particularly Conditional Random Fields, which have been shown in [3] to be best-suited for this ...cently, ... Voir le document complet
6
Image and video text recognition using convolutional neural networks
... supposed to be lower than a certain threshold in the case of a character ...according to their gray tone intensity. Some regions are too large and others are too small to be in- stances of ... Voir le document complet
178
De-identification of patient notes with recurrent neural networks
... allowed to access the identified patient notes, thus the task cannot be ...prone to mis- takes. (Neamatullah et al., 2008) asked 14 clinicians to detect PHI in approximately 130 patient notes: the ... Voir le document complet
14
Towards better understanding and improving optimization in recurrent neural networks
... complexity and gradient propagation, but also indirectly influence gradient propagation via the implicit effect of κ = ν + ρ on d as already discussed in Section ...applied to any semi-parametric attentive ... Voir le document complet
109
Singing voice detection with deep recurrent neural networks
... chosen to be the logistic sigmoid or hyperbolic tangent ...MLP, and in ...able to model the dynamic of the input stream, they are thus classifiers that can handle the sequential aspect of input ... Voir le document complet
6
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
... simple Recurrent Neural Network (RNN) and our multimodal Recurrent Neural Network (m-RNN) ...image and its corresponding sentence ...start and an end sign w end to ... Voir le document complet
16
Deep neural networks for audio scene recognition
... k and b k ) relies on supervised methods such as the Back- Propagation (BP) algorithm [12] whose the principle will be reminded in section ...layers and neurons increases, supervised methods are not ... Voir le document complet
6
Unsupervised Speech Unit Discovery Using K-means and Neural Networks
... able to see the usefulness of the poste- riorgrams, which are data obtained by supervised ...sought to obtain these posteriorgrams phones in an unsupervised way. To obtain phone posteriorgrams, ... Voir le document complet
13
Integration of Shape Context and Neural Networks for Symbol Recognition
... for Neural Networks By using shape contexts as input features for neural networks, we loose the ex- plicit similarity measure on which shape context classification is based ...up to the ... Voir le document complet
10
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
... between Recurrent Networks and Turing Machines Dynamical systems (in particular discrete time systems, that is difference equations) are Turing universal (the game “Life" is a cellular automata ... Voir le document complet
16
Regression algorithm for emotion detection
... easy to control for calibration, as opposed to heart-rate or GSR (Galvanic Skin Response, ie, sweat), and emotions are relatively easy for most people to determine from a ...experimental ... Voir le document complet
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