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.

Infant Cry Recognition System

2019 ◽  
Vol 11 (1) ◽  
pp. 15-32
Author(s):  
Yosra Abdulaziz Mohammed
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Acoustic Features ◽  
Infant Cry ◽  
Identification Rate ◽  
The One ◽  
Better Than

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 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.

A CONNECTIONIST APPROACH TO SPEECH RECOGNITION

1993 ◽  
Vol 07 (04) ◽  
pp. 647-667 ◽  
Author(s):  
YOSHUA BENGIO
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Speech Recognition ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Statistical Techniques ◽  
Recurrent Networks ◽  
Phoneme Recognition ◽  
Connectionist Approach

The task discussed in this paper is that of learning to map input sequences to output sequences. In particular, problems of phoneme recognition in continuous speech are considered, but most of the discussed techniques could be applied to other tasks, such as the recognition of sequences of handwritten characters. The systems considered in this paper are based on connectionist models, or artificial neural networks, sometimes combined with statistical techniques for recognition of sequences of patterns, stressing the integration of prior knowledge and learning. Different architectures for sequence and speech recognition are reviewed, including recurrent networks as well as hybrid systems involving hidden Markov models.

Lithofacies classification based on a hybrid system of artificial neural networks and hidden Markov models

2020 ◽  
Vol 221 (3) ◽  
pp. 1484-1498 ◽  
Author(s):  
Runhai Feng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hidden Markov Models ◽  
Hybrid System ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Rock Properties ◽  
Complex Data ◽  
Artificial Neural

SUMMARY Lithofacies is one of the most important reservoir parameters, which could provide a qualitative description for hydrocarbon and geothermal reservoirs. Various techniques, such as artificial neural networks (ANN) and hidden Markov models (HMM), have been applied to extract this information, with the well log suites as inputs. However, both of these methods have their own limitations, such as no geological priors in ANN, since log samples along the depth direction are treated independently. While in HMM, a simple Gaussian assumption is usually adopted, which may not be sufficient for complex data distributions. In order to address these problems, a new method is proposed, which combines ANN and HMM into a hybrid system. This new technique allows for a more flexible approach to the probability distributions of rock properties, without any Gaussian assumption being made. At the same time, the geological dependence between adjacent samples is introduced by a representative transition matrix of lithofacies. The output probability from ANN must be reformulated to an emission function before it can be fed into the HMM, which is achieved via the Bayes’ rule. Then the Viterbi algorithm in HMM is applied for the decoding of sequential patterns in the subsurface. In this fashion, the classification process can be proceeded statistically and geologically. Better performance of the proposed approach, compared with other classification methods, is demonstrated in two case studies.

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.

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