Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework

Sarma, Mousmita; Sarma, Kandarpa Kumar

doi:10.1007/978-81-322-1862-3

Cited by 18 publications

(9 citation statements)

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“…Where is the input to neuron and is weight related to input. An activation function is applied to net giving as a *Corresponding Author result the output of neuron O and amplitude of neuron in output is limited by this function [6].…”

Section: B Artificial Neural Network (Ann)mentioning

confidence: 99%

A Novel Approach for Computer Assisted Sleep Scoring Mechanism using ANN

Farooq¹,

Jain²,

Sharma³

et al. 2020

IJACSA

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Sleep analysis and its categories in sleep scoring system is considered to be helpful in an area of sleep research and sleep medicine. The scheduled study employs novel approach for computer assisted automated sleep scoring system using physiological signals and Artificial neural network. The data collected were recorded for seven hour, 30 second epoch for each subject. The data procured from the physiological signal was controlled and prepared to expel degenerated signals in order to extract essential data or features used for the study. As, it is known human body distributes its own electrical signals which is needed to be eliminated and these are known as artifacts and they are needed to be filtered out. In this study, signal filtering is achieved by using Butterworth Low-Pass filter. The features extracted were trained and classified using an Artificial Neural Network classifier. Even though, it is a highly complicated concept, using same in biomedical field when engaged with electrical signals which is obtained from body is novel. The accuracy estimated for the system was found to be good and thus the procedure can be very helpful in clinics, particularly useful for neurologist for diagnosing the sleep disorders.

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Section: B Artificial Neural Network (Ann)mentioning

confidence: 99%

A Novel Approach for Computer Assisted Sleep Scoring Mechanism using ANN

Farooq¹,

Jain²,

Sharma³

et al. 2020

IJACSA

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“…There can be one or more hidden layers which perform an array of different functions. The hidden layers are made up of cells which undertake the function of summing up the output which is a product of preceding layer [14]. This summing is done after multiplying it by weight vector through neurons, Hence, each cell or anode gives a resultant output with the given input according to a non-linear transfer function which is known as the activation function [15].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review

Farooq¹

2017

IJSSN

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Abstract:Sleep is an essential element for an individual's well-being and is considered vital for the overall mental and physical heath of a person. Sleep can be considered as a virtual detachment of an individual from his environment. In normal humans, about 30% of their life-time is spent for sleep. Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Sleep scoring is under taken by the examination and visual inspection of polysomnograms (PSG) done by sleep specialist. PSG is specialty test, the conduction of which includes the recording of various physiological signals. The signals obtained are processed using digital processing tools so as to extract information. Soft computing techniques are used to analyze the signals. ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparations of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present. The high performances observed with systems based onneural networks highlight that these tools may be act new tools in the field of sleep research. In this scenario we are surmised the review regarding the computer assisted automatic scoring of sleep and soft computing technique Artificial Neural Network.

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“…Several algorithms have been tested in ASR systems. The hidden Markov model (HMM) is the one mostly used [1] and its application always stirs interest to researchers working in ASR systems [2,3]. Other algorithms such as support vector machines (SVMs) have also been used and have yielded good results [4,5].…”

Section: Introductionmentioning

confidence: 99%

Recognition of isolated digits using DNN–HMM and harmonic noise model

Guerid¹,

Houacine

2019

IET signal process.

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Speech recognition is an area that is constantly developing. In this study, the authors present a new system of speech recognition applied to the Arabic language. The system proposed here is based on the harmonic plus noise model (HNM). This model is rather used in speech synthesis tasks and is known for providing excellent speech production quality. Thus, their contribution lies in replacing the conventional mel-frequency cepstrum coefficients (MFCC) parameters with a set of acoustic parameters, extracted through the HNM estimation process. The HNM model allows development of a more adapted processing by distinguishing voiced and unvoiced speech frames and by characterising the harmonic property of speech. As common, their system consists of both training and recognition phases. Deep neural networks and hidden Markov models (DNN-HMM) are used for modelling the voiced frames corresponding to the harmonic part. The DNN model is estimated with static and dynamic parameters. Moreover, the unvoiced frames, which represent the noise part of the HNM, are clustered with an HMM model. The spoken Arabic digits are used to measure the performance of the proposed recognition system and a comparison with the MFCC-based approach is performed.

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Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework

Cited by 18 publications

References 0 publications

A Novel Approach for Computer Assisted Sleep Scoring Mechanism using ANN

A Novel Approach for Computer Assisted Sleep Scoring Mechanism using ANN

A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review

Recognition of isolated digits using DNN–HMM and harmonic noise model

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