Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection

Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection

This study examines how ten different audio features, including MFCC, mel-spectrogram, chroma, and spectral contrast etc., influence speaker change detection (SCD) performance. The analysis is conducted using two unsupervised methods: Bayesian information criterion with Gaussian mixture model (BIC-G...

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Bibliographic Details
Main Authors: Alymzhan Toleu, Gulmira Tolegen, Rustam Mussabayev, Alexander Krassovitskiy, Bagashar Zhumazhanov
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
speaker change detection
audio features
unsupervised approach
Online Access:https://www.mdpi.com/2076-3417/14/24/12026
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