Channel and channel subband selection for speaker diarization

Ahmed, Ahmed Isam; Chiverton, John; Ndzi, David; Al-Faris, Mahmoud

doi:10.1016/j.csl.2022.101367

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…Efficient communication methods among humans and computer are challenging in advanced technologies. The easiest way for the community to enter information is by signals of speech [3]. Hence, the speech signal processing method and its tools are the most important things in information society.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

2024

IJIES

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An optimized enhanced-multi learner approach towards speaker identification based on single-sound segments

Shahamiri

2023

Multimed Tools Appl

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Speaker Identification (SI) is the task of identifying an unknown speaker of an utterance by comparing the voice biometrics of the unknown speaker with previously stored and known speaker models. Although deep learning algorithms have been successful in different speech and speaker recognition systems, they are computationally expensive and require considerable run-time resources. This paper approaches this issue by proposing an optimized text-independent SI system based on convolutional neural networks (CNNs) that not only delivers accuracies on par with state-of-the-art benchmarks but also demands significantly fewer trainable parameters. The proposed system integrates an Enhanced Multi-Active Learner framework, which distributes the complexity of the learning task among an array of learners, with a novel SI approach in which speakers are identified based on a single sound segment of voice biometrics. Here, experiments were conducted with all 1881 VoxCeleb 1 and TIMIT speakers, and results were compared with the SI systems reported in the literature that were assessed on the same speakers’ data. Results indicate that first, the proposed system outperformed the benchmark systems’ performances by delivering up to 2.43% better top-1 accuracy, and second, it reduced the number of deep learning trainable parameters by up to 95%. The proposed SI could bring offline, large-scale speaker identification to low-end computing machines without specific deep learning hardware and make the technology more affordable.

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Optimized speaker change detection approach for speaker segmentation towards speaker diarization based on deep learning

VijayKumar¹,

2023

Data & Knowledge Engineering

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Channel and channel subband selection for speaker diarization

Cited by 3 publications

References 38 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

An optimized enhanced-multi learner approach towards speaker identification based on single-sound segments

Optimized speaker change detection approach for speaker segmentation towards speaker diarization based on deep learning

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