A Two-Stage Hierarchical Multilingual Emotion Recognition System Using Hidden Markov Models and Neural Networks

2017 ◽  
Author(s):  
Ahmed H. Abo absa ◽  
M. Deriche
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Two Stage

Infant Cry Recognition System

2022 ◽  
pp. 629-647
Author(s):  
Yosra Abdulaziz Mohammed
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Acoustic Features ◽  
Infant Cry ◽  
Identification Rate ◽  
The One

Cries of infants can be seen as an indicator of pain. It has been proven that crying caused by pain, hunger, fear, stress, etc., show different cry patterns. The work presented here introduces a comparative study between the performance of two different classification techniques implemented in an automatic classification system for identifying two types of infants' cries, pain, and non-pain. The techniques are namely, Continuous Hidden Markov Models (CHMM) and Artificial Neural Networks (ANN). Two different sets of acoustic features were extracted from the cry samples, those are MFCC and LPCC, the feature vectors generated by each were eventually fed into the classification module for the purpose of training and testing. The results of this work showed that the system based on CDHMM have better performance than that based on ANN. CDHMM gives the best identification rate at 96.1%, which is much higher than 79% of ANN whereby in general the system based on MFCC features performed better than the one that utilizes LPCC features.

scholarly journals A NEW VECTOR QUANTIZATION APPROACH FOR DISCRETE HMM SPEECH RECOGNITION SYSTEM

2014 ◽  
pp. 72-78
Author(s):  
Mohamed Debyeche ◽  
Jean Paul Haton ◽  
Amrane Houacine
Keyword(s):  
Neural Networks ◽  
Hidden Markov Models ◽  
Vector Quantization ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Speech Recognition System ◽  
Optimal Distribution ◽  
Micro Structure ◽  
Baseline System

In order to address accuracy issues of discrete Hidden Markov Models (HMMs), in this paper, a new vector quantization (VQ) approach is presented. This new VQ approach performs an optimal distribution of VQ codebook components on HMM states. This technique that we named the distributed vector quantization (DVQ) of hidden Markov models, succeeds in unifying acoustic micro-structure and phonetic macro-structure, when the estimation of HMM parameters is performed. The DVQ technique is implemented through two variants. The first variant uses the K-means algorithm (K-means-DVQ) to optimize the VQ, while the second variant exploits the benefits of the classification behavior of neural networks (NN-DVQ) for the same purpose. The proposed variants are compared with the HMMbased baseline system by experiments of specific Arabic consonants recognition. The results show that the distributed vector quantization technique increase the performance of the discrete HMM system.

scholarly journals A New Data Fusion Method for Hybrid MMC/RNA Learning : Application to Automatic Speech Recognition

2005 ◽  
Vol Volume 3, Special Issue... ◽  
Author(s):  
Lilia Lazli ◽  
Mohamed Tayeb Laskri
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Data Fusion ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Training Data ◽  
Nous Rapportons ◽  
The One

International audience It is well known that traditional Hidden Markov Models (HMM) systems lead to a considerable improvement when more training data or more parameters are used. However, using more data with hybrid Hidden Markov Models and Artificial Neural Networks (HMM/ANN) models results in increased training times without improvements in performance. We developed in this work a new method based on automatically separating data into several sets and training several neural networks of Multi-Layer Perceptrons (MLP) type on each set. During the recognition phase, models are combined using several criteria (based on data fusion techniques) to provide the recognized word. We showed in this paper that this method significantly improved the recognition accuracy. This method was applied in an Arabic speech recognition system. This last is based on the one hand, on a fuzzy clustering (application of the fuzzy c-means algorithm) and of another share, on a segmentation at base of the genetic algorithms. De nombreuses expériences ont déjà montré qu'une forte amélioration du taux de reconnaissance des systèmes MMC (Modèles de Markov Cachés) traditionnels est observée lorsque plus de données d'apprentissage sont utilisées. En revanche, l'augmentation du nombre de données d'apprentissage pour les modèles hybrides MMC/RNA (Modèles de Markov cachés/Réseaux de Neurones Artificiels) s'accompagne d'une forte augmentation du temps nécessaire à l'apprentissage des modèles, mais pas ou peu des performances du système. Pour pallier cette limitation, nous rapportons dans ce papier les résultats obtenus avec une nouvelle méthode d'apprentissage basée sur la fusion de données. Cette méthode a été appliquée dans un système de reconnaissance de la parole arabe. Ce dernier est basé d'une part, sur une segmentation floue (application de l'algorithme c-moyennes floues) et d'une autre part, sur une segmentation à base des algorithmes génétiques.

scholarly journals FIND: Identifying Functionally and Structurally Important Features in Protein Sequences with Deep Neural Networks

2019 ◽  
Author(s):  
Ranjani Murali ◽  
James Hemp ◽  
Victoria Orphan ◽  
Yonatan Bisk
Keyword(s):  
Neural Networks ◽  
Amino Acid ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Genomic Sequence ◽  
Hidden Markov ◽  
Amino Acid Sequences ◽  
Homologous Proteins ◽  
Biological Studies ◽  
Insight Into

AbstractThe ability to correctly predict the functional role of proteins from their amino acid sequences would significantly advance biological studies at the molecular level by improving our ability to understand the biochemical capability of biological organisms from their genomic sequence. Existing methods that are geared towards protein function prediction or annotation mostly use alignment-based approaches and probabilistic models such as Hidden-Markov Models. In this work we introduce a deep learning architecture (FunctionIdentification withNeuralDescriptions orFIND) which performs protein annotation from primary sequence. The accuracy of our methods matches state of the art techniques, such as protein classifiers based on Hidden Markov Models. Further, our approach allows for model introspection via a neural attention mechanism, which weights parts of the amino acid sequence proportionally to their relevance for functional assignment. In this way, the attention weights automatically uncover structurally and functionally relevant features of the classified protein and find novel functional motifs in previously uncharacterized proteins. While this model is applicable to any database of proteins, we chose to apply this model to superfamilies of homologous proteins, with the aim of extracting features inherent to divergent protein families within a larger superfamily. This provided insight into the functional diversification of an enzyme superfamily and its adaptation to different physiological contexts. We tested our approach on three families (nitrogenases, cytochromebd-type oxygen reductases and heme-copper oxygen reductases) and present a detailed analysis of the sequence characteristics identified in previously characterized proteins in the heme-copper oxygen reductase (HCO) superfamily. These are correlated with their catalytic relevance and evolutionary history. FIND was then applied to discover features in previously uncharacterized members of the HCO superfamily, providing insight into their unique sequence features. This modeling approach demonstrates the power of neural networks to recognize patterns in large datasets and can be utilized to discover biochemically and structurally important features in proteins from their amino acid sequences.Author summary

A New Modular Strategy For Action Sequence Automation Using Neural Networks And Hidden Markov Models

2013 ◽  
Vol 2 (3) ◽  
pp. 18-35 ◽  
Author(s):  
Mohamed Adel Taher ◽  
Mostapha Abdeljawad
Keyword(s):  
Neural Networks ◽  
Hidden Markov Models ◽  
Fuzzy Systems ◽  
Markov Models ◽  
Hidden Markov ◽  
Current System ◽  
Action Sequence ◽  
Hybrid Strategy ◽  
Automatic Control Theory ◽  
Welding Automation

In this paper, the authors propose a new hybrid strategy (using artificial neural networks and hidden Markov models) for skill automation. The strategy is based on the concept of using an “adaptive desired” that is introduced in the paper. The authors explain how using an adaptive desired can help a system for which an explicit model is not available or is difficult to obtain to smartly cope with environmental disturbances without requiring explicit rules specification (as with fuzzy systems). At the same time, unlike the currently available hidden Markov-based systems, the system does not merely replay a memorized skill. Instead, it takes into account the current system state as reported by sensors. The authors approach can be considered a bridge between the spirit of conventional automatic control theory and fuzzy/hidden Markov-based thinking. To demonstrate the different aspects of the proposed strategy, the authors discuss its application to underwater welding automation.

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