Novel Architectures For Unsupervised Information Bottleneck Based Speaker Diarization Of Meetings

Abstract

Speaker diarization is an important problem that is topical, and is especially useful as a preprocessor for conversational speech related applications. The objective of this paper is two-fold: (i) segment initialization by uniformly distributing speaker information across the initial segments, and (ii) incorporating speaker discriminative features within the unsupervised diarization framework. In the first part of the work, a varying length segment initialization technique for Information Bottleneck (IB) based speaker diarization system using phoneme rate as the side information is proposed. This initialization distributes speaker information uniformly across the segments and provides a better starting point for IB based clustering. In the second part of the work, we present a Two-Pass Information Bottleneck (TPIB) based speaker diarization system that incorporates speaker discriminative features during the process of diarization. The TPIB based speaker diarization system has shown imp

Stats

• citations11
• S2 citations—
• github stars0
• HF likes0
• heat score8.09
• arxiv keydawalatabad2020novel

Related papers

• Incremental Transfer Learning In Two-pass Information Bottleneck Based Speaker Diarization System For Meetings (2019)6.34
• Exploring Speaker-related Information In Spoken Language Understanding For Better Speaker Diarization (2023)0.00
• Enhancements For Audio-only Diarization Systems (2019)0.00
• Speaker Diarization Using Two-pass Leave-one-out Gaussian PLDA Clustering Of DNN Embeddings (2021)2.26
• Integrating Audio, Visual, And Semantic Information For Enhanced Multimodal Speaker Diarization (2024)0.00
• Overlap-aware Low-latency Online Speaker Diarization Based On End-to-end Local Segmentation (2021)10.35
• Learning Deep Representations By Multilayer Bootstrap Networks For Speaker Diarization (2019)0.00
• End-to-end Speaker Diarization As Post-processing (2020)11.08