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20192026
most citedGraph-PIT: Generalized permutation invariant training for continuous separation of arbitrary numbers of speakers

20 citations · 33 across the 9 of their papers we have counts for

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10 papers · 1 filter

eess.AS20261 cited

Speech Quality-Based Localization of Low-Quality Speech and Text-to-Speech Synthesis Artefacts

Michael Kuhlmann, Alexander Werning, Thilo von Neumann +1

A large number of works view the automatic assessment of speech from an utterance- or system-level perspective. While such approaches are good in judging overall quality, they cann…

eess.AS2026

On the Role of Spatial Features in Foundation-Model-Based Speaker Diarization

Marc Deegen, Tobias Gburrek, Tobias Cord-Landwehr +4

Recent advances in speaker diarization exploit large pretrained foundation models, such as WavLM, to achieve state-of-the-art performance on multiple datasets. Systems like DiariZe…

eess.AS2025

Error Analysis in a Modular Meeting Transcription System

Peter Vieting, Simon Berger, Thilo von Neumann +3

Meeting transcription is a field of high relevance and remarkable progress in recent years. Still, challenges remain that limit its performance. In this work, we extend a previousl…

eess.AS2022

MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator

Tobias Cord-Landwehr, Thilo von Neumann, Christoph Boeddeker +1

The scope of speech enhancement has changed from a monolithic view of single, independent tasks, to a joint processing of complex conversational speech recordings. Training and eva…

eess.AS20222 cited

A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network

Tobias Gburrek, Christoph Boeddeker, Thilo von Neumann +3

We propose a system that transcribes the conversation of a typical meeting scenario that is captured by a set of initially unsynchronized microphone arrays at unknown positions. It…

eess.AS20216 cited

Speeding Up Permutation Invariant Training for Source Separation

Thilo von Neumann, Christoph Boeddeker, Keisuke Kinoshita +2

Permutation invariant training (PIT) is a widely used training criterion for neural network-based source separation, used for both utterance-level separation with utterance-level P…