Optimal trained artificial neural network for Telugu speaker diarization

Sethuram, V.; Prasad, Ande; Rao, R. Rajeshwara

doi:10.1007/s12065-020-00378-9

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…Sethuram [14] implemented a novel MFCCbased FE method of the speaker diarization (Telugu language) model. Later, an Optimized Artificial Neural Network (ANN) was introduced for the process of clustering.…”

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. The Feature-Level fusion[15] estimated only the DER and it achieved the 4.31 on ELSDSR dataset.…”

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confidence: 98%