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An Interactive MFCC-Driven Hierarchical Clustering Framework for Automatic Speaker Diarization with Visual Analytics
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 77 |
| Year of Publication: 2026 |
| Authors: Sayyada Sara Banu, Ratnadeep R. Deshmukh |
10.5120/ijca2026926226
|
Sayyada Sara Banu, Ratnadeep R. Deshmukh . An Interactive MFCC-Driven Hierarchical Clustering Framework for Automatic Speaker Diarization with Visual Analytics. International Journal of Computer Applications. 187, 77 ( Jan 2026), 28-34. DOI=10.5120/ijca2026926226
Abstract
Automatic Speaker Diarization (ASD) is the task of determining “who spoke when” in multi-speaker audio recordings without prior speaker labels. This paper presents a transparent, tunable, and GUI-driven diarization framework that integrates MFCC + Δ + Δ² embeddings, adaptive percentile-based Voice Activity Detection (VAD), and Agglomerative Hierarchical Clustering (AHC) with configurable distance metrics and linkage strategies. The system provides complete control over preprocessing, segmentation, clustering, and post-processing, while offering rich visual analytics including waveform-aligned speaker timelines, spectrograms, MFCC heatmaps, PCA-based embedding scatter plots, Silhouette-driven cluster diagnostics, and conversational metrics. Experimental evaluation shows that the proposed MFCC + AHC pipeline achieves stable speaker grouping with clear cluster separation and reduced fragmentation after post-processing, achieving a diarization error rate between 5.8% and 8.1% on test recordings. The tool supports RTTM/CSV/JSON export and is suitable for research, education, conversational analysis, and domain-specific diarization studies requiring interpretability and flexibility.
References
- Anguera, Xavier, et al. “Speaker Diarization: A Review of Recent Research.” IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, no. 2, 2012, pp. 356–370.
- Tranter, Stuart, and Douglas Reynolds. “An Overview of Automatic Speaker Diarization Systems.” IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, no. 5, 2006, pp. 1557–1565.
- Kenny, Patrick, et al. “A Study of Interspeaker Variability in Speaker Verification.” IEEE Transactions on Audio, Speech, and Language Processing, vol. 16, no. 5, 2008, pp. 980–988.
- Reynolds, Douglas A., and Richard C. Rose. “Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models.” IEEE Transactions on Speech and Audio Processing, vol. 3, no. 1, 1995, pp. 72–83.
- Bimbot, Frederic, et al. “A Tutorial on Text-Independent Speaker Verification.” EURASIP Journal on Advances in Signal Processing, 2004.
- Wajahat, Md, and Tanvir Habib. “Voice Activity Detection Using Short-Time Energy and Zero-Crossing Rate for Speech Enhancement.” International Journal of Computer Applications, vol. 179, no. 23, 2018.
- Sadjadi, Seyed Omid, and John HL Hansen. “Unsupervised Noise Robustness Improvement for Voice Activity Detection Using Voicing Measures.” IEEE Signal Processing Letters, vol. 20, no. 3, 2013, pp. 197–200.
- Kinnunen, Tomi, and Haizhou Li. “An Overview of Text-Independent Speaker Recognition: From Features to Supervectors.” Speech Communication, vol. 52, no. 1, 2010, pp. 12–40.
- Davis, Steven, and Paul Mermelstein. “Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences.” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 28, no. 4, 1980, pp. 357–366.
- Zhang, Chenpeng, et al. “Robust Speaker Clustering Using Cluster Purity.” Interspeech, 2013.
- El-Khoury, Jérémy, et al. “Enhancement of Speaker Diarization: A Comparison of Clustering Methods.” IEEE ICASSP, 2009.
- Tóth, László. “Hierarchical Clustering in Speech Technology.” Acta Cybernetica, vol. 16, no. 1, 2003, pp. 1–12.
- Garofolo, John S., et al. “The Rich Transcription 2004 Meeting Recognition Evaluation.” NIST RT04, 2004.
- Carletta, Jean. “Unleashing the AMI Meeting Corpus.” Machine Learning, vol. 68, no. 2, 2007, pp. 155–173.
- Ryant, Neville, et al. “The First DIHARD Speech Diarization Challenge.” Interspeech, 2018.
- Sell, Gregory, and Daniel Garcia-Romero. “Speaker Diarization with PLDA i-Vector Scoring and Unsupervised Calibration.” IEEE SLT, 2014.
- Snyder, David, et al. “X-Vectors: Robust DNN Embeddings for Speaker Recognition.” ICASSP, 2018.
- Desplanques, Brecht, et al. “ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification.” Interspeech, 2020.
- Hsu, Wei-Ning, et al. “HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units.” IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021.
- Baevski, Alexei, et al. “Wav2Vec 2.0: A Framework for Self-Supervised Learning of Speech Representations.” NeurIPS, 2020.
- Chen, Yao, et al. “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.” arXiv preprint arXiv:1802.03426, 2018.
- Jolliffe, Ian T., and Jorge Cadima. “Principal Component Analysis: A Review and Recent Developments.” Philosophical Transactions of the Royal Society A, vol. 374, no. 2065, 2016.
- El-Shafey, Laurent. “PLDA with Two Sources of Inter-Session Variability.” IEEE Transactions on Audio, Speech, and Language Processing, 2013.
- Bozonnet, Sébastien, et al. “Improved Speaker Diarization Using Speaker Role Information.” Interspeech, 2012.
- Anguera, Xavier, and Chuck Wooters. “Frame Level Clustering of Acoustic Features for Speaker Diarization.” Interspeech, 2006.
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