Text-independent speaker identification based on selection of the most similar feature vectors

Soleymanpour, Mohammad; Marvi, Hossein

doi:10.1007/s10772-016-9385-x

Cited by 33 publications

(17 citation statements)

References 16 publications

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“…e handling of poems can actually be regarded as a text handling problem with a special content in form [8]. Literature [9] developed a "Poetry Computer Aided Research System," which takes the vocabulary in the poem as the main research unit to realize word retrieval, word frequency statistics, and image indexing.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Poetry Style Based on Text Classification Algorithm

Can

2022

Scientific Programming

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In order to realize the effect of intelligent poetry style analysis, this paper applies the text classification algorithm to the poetry style analysis, combines the knowledge representation algorithm to perform text classification and recognition, improves the algorithm, and applies it to the poetry style analysis model. Moreover, this paper combines intelligent algorithms to construct a poetry style analysis system, constructs the system’s functional modules, preprocesses the poetry documents in the corpus, and maps them to the vector space that can be directly processed by the computer. In addition, after constructing the system model, this paper verifies the poetry style analysis system based on the text classification algorithm through simulation experiments. From the research results, the effect of the poetry style analysis method based on the text classification algorithm proposed in this paper is very good, which meets the actual needs of poetry style analysis.

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Section: Related Workmentioning

confidence: 99%

Analysis of Poetry Style Based on Text Classification Algorithm

Can

2022

Scientific Programming

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“…e literature proposed to use word vectors to expand the query language model. is model uses word vectors as nonquery word selection and weight evaluation and applies this method to the pseudorelevance feedback process [14].…”

Section: Related Workmentioning

confidence: 99%

“…The literature proposed to use word vectors to expand the query language model. This model uses word vectors as nonquery word selection and weight evaluation and applies this method to the pseudorelevance feedback process [ 14 ]. The experimental results show that this method can select more semantically related expansion words to improve retrieval performance.…”

Section: Related Workmentioning

confidence: 99%

Simulation of English Word Order Sorting Based on Semionline Model and Artificial Intelligence

Meng

2022

Computational Intelligence and Neuroscience

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To improve the word order ranking effect of English language retrieval, based on machine learning algorithms, this paper combines a semionline model to construct an artificial intelligence ranking model for English word order based on a semionline model and establishes a semisupervised ELM regression model. Moreover, this paper derives the mathematical model of semisupervised ELM in detail and uses FCM clustering to screen credible samples, ELM collaborative training to mark each other’s samples, and the marked samples to calculate the output weights of semisupervised ELM regression. In addition, based on continuous learning of OSELMR, this paper uses confidence evaluation to screen out credible unlabeled samples, OSELM collaborative training to mark the credible samples with each other, and credible unlabeled samples to calculate the output weight of SSOSELMR. Finally, this paper designs a control experiment to analyze the model algorithm, compares and counts the parameters, and draws a statistical graph. The research results show that the model constructed in this paper is effective.

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“…However, this strategy did not suit all kinds of audio corpus. Soleymanpour & Marvi [11] proposed text-independent SV depending on the decision of the maximum identical features. The MFCC of each window over given voice utterance has been used as features and the features having the highest similarity were acquired via the clustering technique.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Optimized Deep Learner with Lightweighted Encryption-Based Speaker Verification

Rathinaraja¹,

Chandra²

2021

Preprint

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In the modern digitalized world, Speaker verification (SV) system is essential for authorizing the client’s credentials. To design an effective SV system, MGWOVSW-CAES-GMM system has been proposed. In this system, the Modified Grey Wolf Optimization (MGWO) technique was employed to optimize the variable sliding window size, FMPM features and training variables. The optimized features were watermarked and encrypted using a Chaotic-based Advanced Encryption Standard (CAES). Once the encryption process was completed, the encrypted features were forwarded to the recipient who executes the decryption and de-watermarking processes. At last, the decrypted features were classified using Gaussian Mixture Model (GMM) classifier. Conversely, MGWO has poor convergence rate and ineffective searching results. Hence, this article proposes an EEHOVSW-CAES-GMM system in which Enhanced Elephant Herding Optimization (EEHO) algorithm is applied instead of MGWO. On the contrary, the computational complexity of GMM classifier is high and its efficiency is less while increasing the number of features. For this reason, a Deep Neural Network (DNN) classifier is employed instead of GMM for recognizing the decrypted features and authorize the speaker’s identity. Besides, the parameters utilized in DNN topology are optimized using two different systems such as MGWOVSW-CAES-DNN and EEHOVSW-CAES-DNN for reducing the computational complexity and increasing the classification accuracy effectively when using more number of features. By using these classifiers, the speaker’s identity is verified and the attacks during the transmission are prevented with the highest security level.

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Text-independent speaker identification based on selection of the most similar feature vectors

Cited by 33 publications

References 16 publications

Analysis of Poetry Style Based on Text Classification Algorithm

Analysis of Poetry Style Based on Text Classification Algorithm

Simulation of English Word Order Sorting Based on Semionline Model and Artificial Intelligence

A Novel Optimized Deep Learner with Lightweighted Encryption-Based Speaker Verification

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