QDM-SSD: Quality-Aware Dynamic Masking for Separation-Based Speaker Diarization

Niu, Shutong; Du, Jun; Sun, Lei; Hu, Yu; Lee, Chin-Hui

doi:10.1109/taslp.2023.3244513

Cited by 3 publications

(2 citation statements)

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“…However, there was no possibility to adjust the system parameters and was dependent on the service provider infrastructure. Niu [18] developed an Iterative based SSD with QDM, named QDM-SSD. The QDM-SSD enhanced the simulated data utilized for the conversion of model by QDM to reduce an error effect in the priority of speaker.…”

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%

“…The Feature-Level fusion[15] estimated only the DER and it achieved the 4.31 on ELSDSR dataset. The QDM-SSD[18] had achieved the DER of 18.56 on DIHARD dataset. SSC-PIC[19] had achieved the DER of 7.0 on CALLHOME dataset.…”

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confidence: 97%