Spoken Language Identification Based on Particle Swarm Optimisation–Extreme Learning Machine Approach

Albadr, Musatafa Abbas Abbood; Tiun, Sabrina

doi:10.1007/s00034-020-01388-9

Cited by 44 publications

(14 citation statements)

References 29 publications

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“…Musatafa Abbas et al, [3] Due to the random selection of weights inside the input hidden layer in the whole learning process of this model is not fully effective (i.e., optimised). The LID learning model used in this work is ELM, which is focused on extracting standard features.…”

Section: Related Workmentioning

confidence: 99%

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Spoken Language Recognization Based on Features and Classification Methods

Bam

Degadwala²,

Upadhyay

et al. 2022

IJSRCSEIT

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In Western countries, speech-recognition applications are accepted. In East Asia, it isn't as common. The complexity of the language might be one of the main reasons for this latency. Furthermore, multilingual nations such as India must be considered in order to achieve language recognition (words and phrases) utilizing speech signals. In the last decade, experts have been clamoring for more study on speech. In the initial part of the pre-processing step, a pitch and audio feature extraction technique were used, followed by a deep learning classification method, to properly identify the spoken language. Various feature extraction approaches will be discussed in this review, along with their advantages and disadvantages. Purpose of this research is to Learn transfer learning approaches like Alexnet, VGGNet, and ResNet & CNN etc. using CNN model we got best accuracy for Language Recognition.

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Section: Related Workmentioning

confidence: 99%

“…There is a link to the data in [3] that may be found at (https://doi.org/10.6084/m9.figshare.6015173.v1).Addi tional data may be found on the author's website (http://www.ftsm.ukm.my/sabrina/resource.html), where it can be accessed.…”

Section: Related Workmentioning

confidence: 99%

Spoken Language Recognization Based on Features and Classification Methods

Bam

Degadwala²,

Upadhyay

et al. 2022

IJSRCSEIT

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“…The ELM is an offline supervised batch learning algorithm that requires the availability of all data samples in order to perform the training process [40], [41]. However, all data are not available at once in the realistic application, where data are collected in packets over time.…”

Section: Online Sequential Extreme Learning Machine (Oselm)mentioning

confidence: 99%

Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

et al. 2021

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In the last decade, the implementation of machine learning algorithms in the analysis of voice disorder is paramount in order to provide a non-invasive voice pathology detection by only using audio signal. In spite of that, most recent systems of voice pathology work on a limited acoustic database. In other words, the systems use one vowel, such as /a/, and ignore sentences and other vowels when analyzing the audio signal. Other key issues that should be considered in the systems are accuracy and time consumption of an algorithm. Online Sequential Extreme Learning Machine (OSELM) is one of the machine learning algorithms that can be regarded as a rapid and accurate algorithm in the classification process. Therefore, this paper presents a voice pathology detection and classification system by using OSELM algorithm as a classifier, and Mel-frequency cepstral coefficient (MFCC) as a featured extraction. In this work, the voice samples were taken from the Saarbrücken voice database (SVD). This system involves two parts of the database; the first part includes all voices in SVD with sentences and vowels /a/, /i/, and /u/, which are uttered in high, low, and normal pitches; and the second part utilizes voice samples of the common three types of pathologies (cyst, polyp, and paralysis) based on the vowel /a/ that is produced in normal pitch. The experimental results have shown that OSELM was able to achieve the highest accuracy up to 91.17%, 94% of precision, and 91% of recall. Furthermore, OSELM obtained 87%, 87.55%, and 97.67% for f-measure, G-mean, and specificity, respectively. The proposed system also presents a high ability to achieve detection and classification results in real-time clinical applications.

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“…The past few decades have witnessed an increasing interest in using nature-inspired algorithms to solve numerous optimization problems including timetabling problems [1][2][3][4]; data mining [5][6][7]; breast cancer diagnosis [8]; load balancing of tasks in cloud computing [9]; language identification [10,11]; and vehicle routing problems [12][13][14]. The observation of processes found in nature became the basis for nature-inspired algorithms of which the main objective is to seek the global optimal solutions for certain problems [15].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Based on Natural Selection Theory for Optimization Problems

et al. 2020

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The metaheuristic genetic algorithm (GA) is based on the natural selection process that falls under the umbrella category of evolutionary algorithms (EA). Genetic algorithms are typically utilized for generating high-quality solutions for search and optimization problems by depending on bio-oriented operators such as selection, crossover, and mutation. However, the GA still suffers from some downsides and needs to be improved so as to attain greater control of exploitation and exploration concerning creating a new population and randomness involvement happening in the population at the solution initialization. Furthermore, the mutation is imposed upon the new chromosomes and hence prevents the achievement of an optimal solution. Therefore, this study presents a new GA that is centered on the natural selection theory and it aims to improve the control of exploitation and exploration. The proposed algorithm is called genetic algorithm based on natural selection theory (GABONST). Two assessments of the GABONST are carried out via (i) application of fifteen renowned benchmark test functions and the comparison of the results with the conventional GA, enhanced ameliorated teaching learning-based optimization (EATLBO), Bat and Bee algorithms. (ii) Apply the GABONST in language identification (LID) through integrating the GABONST with extreme learning machine (ELM) and named (GABONST-ELM). The ELM is considered as one of the most useful learning models for carrying out classifications and regression analysis. The generation of results is carried out grounded upon the LID dataset, which is derived from eight separate languages. The GABONST algorithm has the capability of producing good quality solutions and it also has better control of the exploitation and exploration as compared to the conventional GA, EATLBO, Bat, and Bee algorithms in terms of the statistical assessment. Additionally, the obtained results indicate that (GABONST-ELM)-LID has an effective performance with accuracy reaching up to 99.38%.

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Spoken Language Identification Based on Particle Swarm Optimisation–Extreme Learning Machine Approach

Cited by 44 publications

References 29 publications

Spoken Language Recognization Based on Features and Classification Methods

Spoken Language Recognization Based on Features and Classification Methods

Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

Genetic Algorithm Based on Natural Selection Theory for Optimization Problems

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