BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//EE - ECPv5.10.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:EE
X-ORIGINAL-URL:https://ee.iisc.ac.in
X-WR-CALDESC:Events for EE
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
BEGIN:STANDARD
TZOFFSETFROM:+0530
TZOFFSETTO:+0530
TZNAME:IST
DTSTART:20230101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230721T110000
DTEND;TZID=Asia/Kolkata:20230721T130000
DTSTAMP:20260525T235723
CREATED:20230714T112921Z
LAST-MODIFIED:20230721T031651Z
UID:240869-1689937200-1689944400@ee.iisc.ac.in
SUMMARY:[PhD Colloquium Talk by Prachi Singh] - 21-7 in MMCR\, EE @ 330-430pm {Graph Clustering Approaches for Speaker Diarization of Conversational Speech}
DESCRIPTION:Dear All\,\n\n\nWe are pleased to invite you to the following PhD colloquium talk\,\n\n\n========================== \n \nWho: Ms. Prachi Singh\, PhD candidate\, EE.\n\nWhen: 21/7/2023 at 11AM [Note the updated the time]. High Tea at 1045am\n\nWhere: MMCR\, EE\, IISc and in the Teams Link\n\n\nWhat: Graph Clustering Approaches for Speaker Diarization of Conversational Speech \n\n\nAbstract\nIn this era of advanced machine intelligence\, real-world speech applications need to be equipped to deal with conversations involving multiple speakers. An essential first step in speech information extraction from conversational speech is the task of finding “who spoke when”\, also referred to as speaker diarization. The focus of this talk is to describe our efforts in investigating graph clustering techniques for this problem. While graph models have been used in several other domains\, its application to temporal segmentation of speech is the first of its kind.\n\nThe talk is divided into three main parts. In the first part of this talk\, I will describe a novel proposal on self-supervised learning to perform joint representation learning and clustering\, called self-supervised clustering (SSC) for diarization. On the learned representations\, we explore path integral clustering (PIC)\, a graph-based clustering algorithm. The PIC is an agglomerative graph clustering method that performs clustering based on the edge connections of a node\, called path integral. The proposed SSC with path integral clustering (SSC-PIC) is shown to achieve state-of-the-art performance for benchmark datasets.\n\nThe second part of the talk is an extension of SSC-PIC to incorporate metric learning. We design a neural version of the probabilistic linear discriminant analysis (PLDA) approach with learnable parameters to compute a log-likelihood score between embeddings from two segments of the recording.  We propose a joint self-supervised representation learning and metric learning approach called Selfsup-PLDA-PIC.\n\nIn the third part of the talk\, we introduce an end-to-end supervised graph clustering approach. We develop a supervised learning setup using labeled conversational data for training this model. In this setting\, we propose a supervised clustering approach called Supervised HierArchical gRaph Clustering (SHARC) for speaker diarization. This approach uses Graph Neural Networks (GNN) to capture the similarity between the speaker embeddings and perform hierarchical clustering. An extension of this work is the joint training of the speaker embedding extractor along with the GNN module\, referred to as end-to-end SHARC (E-SHARC). To incorporate overlapped speech detection\, I will illustrate how to extend the E-SHARC model for diarization of overlapped speech recordings.\n\nThe talk will conclude with a summary of our key contributions\, while highlighting the pros and cons of using graph-based models for speaker diarization. \n\n\n==========================\n\n\n\n\n\nAll are welcome
URL:https://ee.iisc.ac.in/event/phd-colloquium-talk-by-prachi-singh-21-7-in-mmcr-ee-330-430pm-graph-clustering-approaches-for-speaker-diarization-of-conversational-speech/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
END:VCALENDAR