Unsupervised Speaker Diarization that is Agnostic to Language, Overlap-Aware, and Tuning Free

Diego Casabuena; Jussi Karlgren; M. Iftekhar Tanveer; Rosie Jones

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Unsupervised Speaker Diarization that is Agnostic to Language, Overlap-Aware, and Tuning Free

M. Iftekhar Tanveer, Diego Casabuena, Jussi Karlgren, Rosie Jones

Computation and Language · Submitted 25 Jul 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our approach shows 79% improvement on purity scores (34% on F-score) against the Google Cloud Platform solution on podcast data.

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arXiv ID2207.12504 (version 1)

Pith IDHCKOYBOU, Pith's permanent record of this paper and its review

CitationDiego Casabuena, Jussi Karlgren, M. Iftekhar Tanveer, Rosie Jones. "Unsupervised Speaker Diarization that is Agnostic to Language, Overlap-Aware, and Tuning Free." arXiv:2207.12504 (2022). https://pith.science/paper/HCKOYBOU

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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,509 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.09 · human participants 0.06 · controlled comparison 0.89 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.97 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; show every role.

Podcasts are conversational in nature and speaker changes are frequent -- requiring speaker diarization for content understanding. We propose an unsupervised technique for speaker diarization without relying on language-specific components. The algorithm is overlap-aware and does not require information about the number of speakers. Our approach shows 79% improvement on purity scores (34% on F-score) against the Google Cloud Platform solution on podcast data.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Podcasts are conversational in nature and speaker changes are frequent -- requiring speaker diarization for content understanding.	background 1.00	0.05	0.02	asserted 1.00
s1	We propose an unsupervised technique for speaker diarization without relying on language-specific components.	contribution 0.98	0.85	0.13	asserted 0.99
s2	The algorithm is overlap-aware and does not require information about the number of speakers.	contribution 0.50	0.65	0.11	asserted 1.00
s3	Our approach shows 79% improvement on purity scores (34% on F-score) against the Google Cloud Platform solution on podcast data.	result 1.00	0.94	0.72	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.88.

What was examined, and what was not

sources read

arXiv listing abstract v1

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

not run

S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

Podcasts are conversational in nature and speaker changes are frequent -- requiring speaker diarization for content understanding. We propose an unsupervised technique for speaker diarization without relying on language-specific components. The algorithm is overlap-aware and does not require information about the number of speakers. Our approach shows 79% improvement on purity scores (34% on F-score) against the Google Cloud Platform solution on podcast data.

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 2 sentences without a confident label

Uses the method or tool of 2 papers

YAMNet: https://tfhub.dev/google/yamnet/1] [ref] to detect non-speech regions in the recording and we set the vectors for non-speech regions to zero.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications · Constructing the Embedding Signal · 1.00
We use Adam [ref] for updating the model parameters.

Adam: A Method for Stochastic Optimization · Solving the optimization problem · 1.00

Uses data or values from 2 papers

The “This American Life Podcast” dataset [ref] consists of $663$ episodes from a podcast with the same name.

Speech Recognition and Multi-Speaker Diarization of Long Conversations · Experiments · 0.96
We conduct the evaluation over the two largest publicly available annotated datasets: This American Life Podcast Dataset [ref] and VoxConverse [ref] .

Spot the conversation: speaker diarisation in the wild · Experiments · 0.90

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