Indian language identification using time-frequency image textural descriptors and GWO-based feature selection

Chowdhury, Amit A.; Borkar, Vaibhav S.; Birajdar, Gajanan K.

doi:10.1080/0952813x.2019.1631392

Cited by 18 publications

(7 citation statements)

References 29 publications

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“…Additionally, other languages were considered such as the Finnish and the Norwegian languages in [6], [7], the potuguese [8] and the indien [9]. These works aimed to identify if the NLI methods earlier used in level two English can be effective to other languages.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Model for Identifying the Arabic Language Learners based on Gated Recurrent Unit Network

Mechti¹,

Alroobaea²,

Krichen³

et al. 2020

IJACSA

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This paper focuses on identifying the Arabic Language learners. The main contribution of the proposed method is to use a deep learning model based on the Gated Recurrent Unit Network (GRUN). The proposed model explores a multitude of stylistic features such as the syntax, the lexical and the ngrams ones. To the best of our awareness, the obtained results outperform those obtained by the best existing systems. Our accuracy is the best comparing with the pioneers (45% vs 41%), considering the limited data and the unavailability of accurate tools dedicated to the Arabic language.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Model for Identifying the Arabic Language Learners based on Gated Recurrent Unit Network

Mechti¹,

Alroobaea²,

Krichen³

et al. 2020

IJACSA

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show abstract

“…The proposed method is compared to the one proposed by Jog et al [72] and the one proposed by Chowdhury et al [73] which is presented in Table (17). Jog et al, in their work, had used a two-stage approach using Cochleagramn visual representation followed by their feature extraction using different texture descriptors.…”

Section: F Comparison With Past Methodsmentioning

confidence: 99%

Hybrid Feature Selection Method Based on Harmony Search and Naked Mole-Rat Algorithms for Spoken Language Identification From Audio Signals

et al. 2020

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This era is dominated by artificial intelligence and its various applications-one of which is Spoken Language Identification (S-LID) which has always been a challenging issue and an important research area in the domain of speech signal processing. This paper deals with SLID to be used for Human-Computer Interaction (HCI) based applications by attempting to classify various languages from three multilingual databases namely CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages, VoxForge and Indian Institute of Technology, Madras (IIT-Madras) speech corpus database by extracting their Mel-Spectrogram features and Relative Spectral Transform-Perceptual Linear Prediction (RASTA-PLP) features. A new hybrid Feature Selection (FS) algorithm have been developed using the versatile Harmony Search (HS) algorithm and a new nature-inspired algorithm called Naked Mole-Rat (NMR) algorithm to select the best subset of features and reduce the model complexity to help it train faster. This selected feature set is fed to five classifiers namely Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Multi-layer Perceptron (MLP), Naïve Bayes (NB) and Random Forest (RF). The evaluation measures used in this paper are precision, recall, f1-score, classification accuracy and number of selected features. An accuracy of 99.89% on CSS10, 98.22% on VoxForge and 99.75% on IIT-Madras speech corpus databases is achieved using RF. Furthermore, the proposed algorithm is found to outperform 15 standard meta-heuristic FS algorithms.

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“…Several FS techniques such as genetic algorithm [14], estimation of distribution algorithm (EDA), and greedy search [15] have been reported in the literature. Chowdhary et al [16], presented a grey wolf optimizer (GWO) FS algorithm for selecting optimum features for improving SLID. Three texture descriptors, local binary pattern (CLBP), local binary pattern histogram Fourier (LBPHF), and discrete wavelet transform calculated from spectrogram images.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…An optimum feature set of 972 and 1141 selected for IITM and IIT-H data sets reported accuracies 92.35% and 100% with computation time of 158 and 182 min, respectively. Guha et al [6] [3,16].…”

Section: Review Of Related Workmentioning

confidence: 99%

Improving Indian Spoken-Language Identification by Feature Selection in Duration Mismatch Framework

Bakshi

Kopparapu

2021

SN COMPUT. SCI.

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Paper presents novel duration normalized feature selection technique and two-step modified hierarchical classifier to improve the accuracy of spoken language identification (SLID) using Indian languages for duration mismatched condition. Feature selection averages random forest-based importance vectors of open SMILE features of different duration utterances. Although it improves the SLID system's accuracy for mismatched training and testing durations, the performance is significantly reduced for short-duration utterances. A cascade of inter-family and intra-family classifiers with an additional class to improve false language family estimation. All India Radio data set with nine Indian languages and different utterance durations was used as speech material. Experimental results showed that 150 optimal features with the proposed modified hierarchical classifier showed the highest accuracy of 96.9% and 84.4% for 30 s and 0.2 s utterances for the same train-test duration. However, we achieved an accuracy of 98.3% and 61.9% for 15 and 0.2 s test duration when trained with 30 s duration utterance. Comparative analysis showed a significant improvement in accuracy than several SLID systems in the literature.

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Indian language identification using time-frequency image textural descriptors and GWO-based feature selection

Cited by 18 publications

References 29 publications

Deep Learning Model for Identifying the Arabic Language Learners based on Gated Recurrent Unit Network

Deep Learning Model for Identifying the Arabic Language Learners based on Gated Recurrent Unit Network

Hybrid Feature Selection Method Based on Harmony Search and Naked Mole-Rat Algorithms for Spoken Language Identification From Audio Signals

Improving Indian Spoken-Language Identification by Feature Selection in Duration Mismatch Framework

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