Extensive Analysis of Global Presidents’ Speeches Using Natural Language

Nivash, S.; Ganesh, E. N.; Harisudha, K.; Sreeram, S.

doi:10.1007/978-981-16-5157-1_65

Cited by 1 publication

(2 citation statements)

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“…For MFCC feature extraction, some algorithms analyze and classify the MFCC features in speech data [30][31][32][33]. Qing et al [32] designed a transfer learning network after extracting MFCC features from the raw speech data.…”

Section: Related Workmentioning

confidence: 99%

“…Step 4: Feedback for Diagnosis (Treatment) study of [33], the MFCC parameters with the best performance in 12 dimensions were extracted to represent the acoustic characteristics of articulation disorders, which were utilized for automatic speech recognition based on the artifcial neural network (ANN). Nivash et al [31] carried out research in 2021 and used a series of machine learning algorithms to classify the MFCC features of speech, such as RF and naive Bayes, and naive Bayes was verifed to be the best algorithm. MFCC was also utilized to detect patients with PD from healthy people.…”

Section: Related Workmentioning

confidence: 99%

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A Triplet Multimodel Transfer Learning Network for Speech Disorder Screening of Parkinson’s Disease

Zhao,

Wang,

Niu

et al. 2024

International Journal of Intelligent Systems

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Deterioration in the quality of a person’s voice and speech is an early sign of Parkinson’s disease (PD). Although a number of computer-based methods have been invested to use patients’ speech for early diagnosis of Parkinson’s disease, they only focus on a fixed pronunciation test, such as the subjects’ monosyllabic pronunciation is analyzed to determine whether they have potential possibility of PD. Moreover, only using traditional speech analysis methods to extract single-view speech features cannot provide a comprehensive feature representation. This paper is dedicated to the study of various pronunciation tests for patients with PD, including the pronunciation of five monosyllabic vowels and a spontaneous dialogue. A triplet multimodel transfer learning network is designed and proposed for identifying subjects with PD in these two groups of tests. First, multisource data extract mel frequency cepstrum coefficient (MFCC) features of speech for preprocessing. Subsequently, a pretrained triplet model represents features from three dimensions as the upstream task of the transfer learning framework. Finally, the pretrained model is reconstructed as a novel model that integrates the triplet model, temporal model, and auxiliary layer as the downstream task, and weights are updated through fine-tuning to identify abnormal speech. Experimental results show that the highest PD detection rates in the two groups of tests are 99% and 90% , respectively, which outperform a large number of internationally popular pattern recognition algorithms and serve as a baseline for other academic researchers in this field.

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