A hybrid <scp>HXPLS‐TMFCC</scp> parameterization and <scp>DCNN‐SFO</scp> clustering based speaker diarization system

Sailaja, C.; Maloji, Suman; Mannepalli, Kasiprasad

doi:10.1002/cpe.6954

Cited by 2 publications

(3 citation statements)

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“…Sailaja [13] developed a segmentation and classification of the diarization of the speaker. This method utilized Tangent weighted Mel-Frequency Cepstral Coefficient (TMFCC) and HXLPS as well as Linear Prediction Coding (LPC) by autocorrelation snapshot for the process of the Feature Extraction (FE).…”

Section: Literature Surveymentioning

confidence: 99%

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A Hybrid DMFCC-LPC Based Feature Extraction with DCNN Clustering for Speaker Diarization

2024

IJIES

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Speaker Diarization (SD) or speaker indexing is a procedure for automatically partitioning a conversation by the number of speakers into homogeneous segments. The trustworthy diarization method accurately estimates the variable length assertion, and it involves major steps such as speech detection, speaker merges, and speaker change. The major problem with the SD method is enhancing the readability of speech transcription. In this study, a hybrid method of feature extraction based on the Dynamic Mel Frequency Cepstral Coefficient (DMFCC) and Linear Prediction Coding (LPC) is proposed for SD. The Voice Activity Detection (VAD) method is utilized to detect the presence or absence of a speaker in the audio lecture, which is followed by speaker segmentation utilizing the extracted features. The Deep Convolutional Neural Network (DCNN) is utilized to determine the feature vector and cluster the speaker from the audio lecture. The results show that the proposed DMFCC-LPC delivers a robust performance on metrics such as accuracy, Diarization Error Rate (DER) and False Positive Rate (FPR) of 0.967, 0.31 and 0.119 on CALLHOME dataset, in contrast to the Speaker Siarization System using HXLP-DCNN with Sailfish Optimization Algorithm (SDS-HXLP-DCNN-SOA), Feature-Level fusion, and Self-supervised clustering with Path Integral Clustering (SSC-PIC).

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Section: Literature Surveymentioning

confidence: 99%

“…Comparison Results HXLP-DCNN-SOA[13] obtained the DER of 0.3 and 0.33 on DIHARD-III and CALLHOME dataset. ANN-ABC-LA[14] had achieved the accuracy of 0.764, FPR of 0.175, FNR of 0.354 and FDR of 351 on EenaduPrathidwani dataset.…”

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confidence: 97%

A Hybrid DMFCC-LPC Based Feature Extraction with DCNN Clustering for Speaker Diarization

2024

IJIES

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“…For hyperparameter tuning of the MSA-CBLSTM algorithm, the SFO algorithm is utilized to improve the classifier results. Sailfish are a group of predators that contribute to harassing and catching their beasts [28]. In the group game, the hunter uses different approaches to assault.…”

Section: Hyperparameter Tuning Using Sfo Algorithmmentioning

confidence: 99%

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Ragab,

Khadidos,

Alshareef

et al. 2023

Sustainability

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Solid biofuels and Internet of Things (IoT) technologies play a vital role in the development of smart cities. Solid biofuels are a renewable and sustainable source of energy obtained from organic materials, such as wood, agricultural residues, and waste. The integration of IoT technology with solid biofuel classification can improve the performance, quality control, and overall management of biofuel production and usage. Recently, machine learning (ML) and deep learning (DL) models can be applied for the solid biofuel classification process. Therefore, this article develops a novel solid biofuel classification using sailfish optimizer hybrid deep learning (SBFC-SFOHDL) model in the IoT platform. The proposed SBFC-SFOHDL methodology focuses on the identification and classification of solid biofuels from agricultural residues in the IoT platform. To achieve this, the SBFC-SFOHDL method performs IoT-based data collection and data preprocessing to transom the input data into a compatible format. Moreover, the SBFC-SFOHDL technique employs the multihead self attention-based convolutional bidirectional long short-term memory model (MSA-CBLSTM) for solid biofuel classification. For improving the classification performance of the MSA-CBLSTM model, the SFO algorithm is utilized as a hyperparameter optimizer. The simulation results of the SBFC-SFOHDL technique are tested and the results are examined under different measures. An extensive comparison study reported the betterment of the SBFC-SFOHDL technique compared to recent DL models.

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A hybrid HXPLS‐TMFCC parameterization and DCNN‐SFO clustering based speaker diarization system

Cited by 2 publications

References 35 publications

A Hybrid DMFCC-LPC Based Feature Extraction with DCNN Clustering for Speaker Diarization

A Hybrid DMFCC-LPC Based Feature Extraction with DCNN Clustering for Speaker Diarization

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

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