Artificial Neural Network-Based Speech Recognition Using Dwt Analysis Applied On Isolated Words From Oriental Languages

Rehmam, Bacha; Halim, Zahid; Abbas, Ghulam; Tufail, Muhammad

doi:10.22452/mjcs.vol28no3.5

Cited by 21 publications

(7 citation statements)

References 35 publications

(43 reference statements)

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“…According to previous works [22,23] and our experiments, embedding dimensiond=3 and time lag τ=1 are good choices to determine the RPS of speech signal. Fig.1 shows the RPSs of six speech signals with different emotions: anger, boredom, disgust, fear, joy, and sadness.…”

Section: ( 1)mentioning

confidence: 85%

“…Unfortunately, such a model cannot convey nonlinear 3D fluid dynamics phenomena of speech [20,21]. In order to fill the existing gap between this ideal linear deterministic model and real strongly unpredictable speech production process, non-linear processing techniques can be used [22,23,24,25].In recent years, reconstructed phase space (RPS) of speech has been used for speech recognition [26,27], speech enhancement [26,27]and detecting sleepiness [30]. Moreover, nonlinear dynamics features extracted from RPS of speech has been employed in SER.It has been shown that geometrical properties of RPScontain important emotional cues of speaker [24,25].…”

Section: Introductionmentioning

confidence: 99%

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Recognition Of Emotion Using Reconstructed Phase Space Of Speech

Harimi

Fakhr

Bakhshi

2016

MJCS

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In recent years, automatic recognition of human's emotion from speech has become one of the most important research areas, which can improve man-machine interaction. In this study, we proposed new features derived from reconstructed phase space (RPS) of speech. To this end, the RPS is uniformly divided into non-overlapping discrete cells and the number of points included in each cell is counted to form the proposed feature vector. Then multiple classifiers were examined to classify speech samples according to their emotional states. Our experimental results have demonstrated the potential and promise of proposed RPS based features as a useful combination for standard prosodic and spectral features. The best average recognition rate of 89.34% was obtained for classifying seven emotion categories in the Berlin database using a support vector machine with both radial basis function and polynomial kernels.Keywords: speech emotion recognition; reconstructed phase space. INTRODUCTIONSpeaking is the fastest and one of the most important communication means of human beings. Now a days, automatic speech recognition (ASR) systems are extensively used in man-machine interaction. However, human speech is generally embedded with emotions to convey the intended message. From this fact arises a new multidisciplinary research area known as Speech emotion recognition (SER). Despite of widespread efforts done in SER, there are many challenging problems that need to be solve in order to improve the performance of SER systems [1]. Based on contradictory reports on the effect of emotions on some of acoustic attributes, specifying effective features is still the major unsolved problem.To solve the above problem, many prosodic and spectral features have been presented. Prosodic features appear when sounds are put together in connected speech and they are mainly deal with intonation, stress and rhythm of speech. It has been shown that prosodic features, which are widely used in SER, suggest important emotional cues of the speaker [1][2][3][4][5][6][7]. In theliterature, pitch, duration, energy and their derivatives are widely used to representprosodic features [1,8]. Features extracted from the spectrum of speech are generally called spectral. These features convey the frequency contents of the signal and provide complementary information for prosodic features Both prosodic and spectral features are generally computed by the traditional linear source-filter model of the human speech production system [19]. Unfortunately, such a model cannot convey nonlinear 3D fluid dynamics phenomena of speech [20,21]. In order to fill the existing gap between this ideal linear deterministic model and real strongly unpredictable speech production process, non-linear processing techniques can be used [22,23,24,25].In recent years, reconstructed phase space (RPS) of speech has been used for speech recognition [26,27], speech enhancement [26,27]and detecting sleepiness [30]. Moreover, nonlinear dynamics features extracted from RPS of speech has b...

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Section: ( 1)mentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Recognition Of Emotion Using Reconstructed Phase Space Of Speech

Harimi

Fakhr

Bakhshi

2016

MJCS

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“…There are different algorithms to train the neural network. The neural network has long been proven to be a good classifier and has been applied successfully in many complex and major classification problems [Bacha Rehmam et al (2015)] [M. Hariharan et al (2010)]. A feed forward neural network with one input layer with thirteen nodes, two hidden layers with thirteen nodes each and one output layer with three nodes is used here in our proposed method.…”

Section: Feed-forward Neural Networkmentioning

confidence: 99%

Plant Diseases Classification Using Feature Reduction, BPNN and Pso

Chanda

2019

IJSECS

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Agriculture is the culture of land and rearing of the plants to provide food for the nourishment and enhancement of life. In India, it is one of the fundamental financial sources; various sorts of plants are cultivated each year. There are various microorganisms that cause many plant diseases and impede normal plant growth. That is the reason from long ago which led researchers to search for new methods of classification of plant diseases. Although there are different neural networks have already been used for plant disease classification, but using these methods alone do not create the best tradeoff between time and precision. So to remove this constraint, we proposed a method for plant disease classification based on Back-propagation Neural Network (BPNN) and Particle Swarm Optimization (PSO). Now we have added some more data to our dataset and applied principal component analysis to reduce the number of total features and on these features we have applied BPNN with PSO. We first train neural network using back-propagation and then we further use PSO to get more optimized weights or fine-tune the parameters of neural network. In our experiment, we have used images of leaves that are infected by various bacterial and fungal diseases: Alternaria alternata, Anthracnose, Bacterial blight, Bacterial leaf scorch, Cercospora leaf spot, and Downy mildew, and our proposed method achieves approximately 96.42 % precision.

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“…The hybrid between the two algorithms mean the position of particle updated, by applying a crossover operation, the data fly to a new search area if it swapped between two particles. So to avoid the local maxima we apply mutation to PSO to increase the diversity of the population [15,16]. Because of the mentioned disadvantages of GA and PSO, in this study, PSO hybrid with GA is proposed to improve the performance of each of those algorithms.…”

Section: Lbg-psoga Algorithmmentioning

confidence: 99%

Speaker Dependent Speech Recognition in Computer Game Control

Al-Irahyim¹,

Abdulkadir²

2017

IJCA

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ABSTRACTp laying has become an integral part of people's lives since the beginning of time, and education games have become an important part of the education process in childhood, for school students and even for university students. Insertion of the voice commands in education games considered a big challenge especially regarding the speech accuracy and rapid response, to achieve this goal an educational game was designed aimed to teach students of Computer Science the fundamental concepts of " logic ", and to enable the game to allow speech input, the game should include the speech recognition system, to build that system, in this study three algorithms for feature extraction are used (MFCC , PLP and Rasta-PLP) with three VQ Code Book generation algorithms (LBG, LBG-PSO and LBG-PSOGA) were studied and applied, and was tested on 864 sound files for different peoples (4 male, 5 female), their ages between (16-30) year, through the results it was noted that when MFCC technique with LBG-PSOGA algorithm was used higher speech accuracy up to 98.5 % was obtained compared to other algorithms and techniques.

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Artificial Neural Network-Based Speech Recognition Using Dwt Analysis Applied On Isolated Words From Oriental Languages

Abstract: Speech recognition is an emerging research area having its focus on human computer interactions (HCI 10,15, and 20 classes, 94.40%, and 91% accuracy ratefor 10,15, and 20 classes, respectively

Cited by 21 publications

References 35 publications

Recognition Of Emotion Using Reconstructed Phase Space Of Speech

Recognition Of Emotion Using Reconstructed Phase Space Of Speech

Plant Diseases Classification Using Feature Reduction, BPNN and Pso

Speaker Dependent Speech Recognition in Computer Game Control

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