Perceptual Features Based Rapid and Robust Language Identification System for Various Indian Classical Languages

Revathi, A.; Jeyalakshmi, C.; Muruganantham, T.

doi:10.1007/978-3-319-71767-8_25

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…The slope is positive in the area to the left of the maximum power point and negative in the area to the right [17]. Many algorithms are used for MPPT Techniques in the grid connected and isolated PV systems [18][19][20][21][22][23][24].…”

Section: Existing Methodologiesmentioning

confidence: 99%

Performance Analysis of PV System For MPPT Using Harmony Search Algorithm

Titus¹,

Sangeetha²,

Ilango³

2019

International Journal of Innovative Research in Electrical, Ele

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This paper proposes a solar PV connected into the single-phase grid connected system with Harmony Search Algorithm (HSA) for tracking the Global maximum peak point. Boost converter is connected to the grid for energy management system. This proposed scheme provides output with improved power quality under the nonlinear load conditions. Program analysis was implemented to find the peak point under various temperature and radiations. The output of the grid voltage and current can be monitored and compared with inverter output. Depends on the change in voltage and current the duty cycle of the dc-dc converter will change by HS algorithm. This proposed work involves PV panels connected in series to track the maximum peak point. The time varying radiation and temperature are fed as the inputs to the PV panel and the normal harmonic search algorithm is used for Maximum Power tracking. It generates a reference PV voltage and from the reference voltage, an optimum duty cycle (β) for the maximum peak is generated. The optimal duty cycle ratio will gives the required output power under nonlinear conditions.

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Section: Existing Methodologiesmentioning

confidence: 99%

Performance Analysis of PV System For MPPT Using Harmony Search Algorithm

Titus¹,

Sangeetha²,

Ilango³

2019

International Journal of Innovative Research in Electrical, Ele

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“…The front‐end processing of LID system includes speech parameterization followed by feature‐extraction 17,18 . Identification is done after language‐modeling at the back‐end of the LID system.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Isolated word language identification system with hybrid features from a deep belief network

Sangwan¹,

Deshwal

Kumar

et al. 2020

Int J Communication

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The representation of good audio features is the first and foremost requirement for improving the identification performance of any system. Most of the representation learning approaches are based on connectionist systems to learn and extract latent features from the speech data. This research work presents a hybrid feature extraction approach to integrate Mel-Frequency Cepstral Coefficients (MFCC) features with Shifted Delta Cepstral (SDC) coefficients features, which are further stacked to Deep Belief Network (DBN), for yielding new feature representations of the speech signals. DBN is utilized for unsupervised feature learning on the extracted MFCC-SDC acoustic features. A 3-layer Back Propagation Neural Network (BPNN) classifier is initialized in terms of the learning outcomes of hidden layers of DBN for identifying language from the uttered speech. The efficiency of the proposed approach is evaluated by simulating several experimental algorithms on the user-defined database of isolated words in four languages, namely, Tamil, Malayalam, Hindi, and English, in the working platform of MATLAB. The obtained results for the proposed hybrid approach MFCC-SDC-DBN are promising. The proposed approach is also compared with the baseline feature extraction approach MFCC-SDC by utilizing traditional acoustic features and BPNN classifier. The accuracy obtained with our proposed approach is 98.1% whereas that of the baseline approach is 82%, thereby providing an overall improvement of 16.1%.

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“…Clustering is a method of grouping ungrouped data. When unlabeled raw data are present, K-means clustering algorithm can be used to group the raw data samples into many clusters [9]. The K-means algorithm identifies a collection in raw data, where the number of groups is represented by the variable "K." The algorithm assigns each data point to the K cluster groups using an iterative approach [2].…”

Section: K-means Clusteringmentioning

confidence: 99%

Isolated Command Recognition Using MFCC and Clustering Algorithm

et al. 2020

Self Cite

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People who cannot walk by themselves need someone to carry them around on a wheelchair. A voice recognition system was developed to recognize a set of commands used by people with disability to control their wheelchair and devices around them. Voice samples of various commands from 8 different speakers were collected. The features of the collected samples were extracted using MFCC feature extraction technique. The MFCC (Mel-frequency cepstral coefficient) feature extraction technique involves pre-emphasis, framing, windowing, FFT, Mel-scale transformation operations. The major reason for choosing MFCC extraction is that the human ear has a response of a logarithmic scale and not a linear scale. Hence, all the framed voice samples are transformed to the Mel-scale with a logarithmic response and stored in the form of K-means clusters. Features extracted from test data are applied to the models developed for commands, and based on the minimum distance criterion, the model is selected to be closely associated with the respective voice command. The voice recognition system was developed for an 8-command model using MATLAB.

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Perceptual Features Based Rapid and Robust Language Identification System for Various Indian Classical Languages

Cited by 13 publications

References 14 publications

Performance Analysis of PV System For MPPT Using Harmony Search Algorithm

Performance Analysis of PV System For MPPT Using Harmony Search Algorithm

Isolated word language identification system with hybrid features from a deep belief network

Isolated Command Recognition Using MFCC and Clustering Algorithm

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