activity
20182024
most citedBlind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation

27 citations · 47 across the 13 of their papers we have counts for

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14 papers · 1 filter

eess.AS2024

Array Geometry-Robust Attention-Based Neural Beamformer for Moving Speakers

Marvin Tammen, Tsubasa Ochiai, Marc Delcroix +3

Although mask-based beamforming is a powerful speech enhancement approach, it often requires manual parameter tuning to handle moving speakers. Recently, this approach was augmente…

eess.AS2023

Lattice Rescoring Based on Large Ensemble of Complementary Neural Language Models

Atsunori Ogawa, Naohiro Tawara, Marc Delcroix +1

We investigate the effectiveness of using a large ensemble of advanced neural language models (NLMs) for lattice rescoring on automatic speech recognition (ASR) hypotheses. Previou…

eess.AS2023

How does end-to-end speech recognition training impact speech enhancement artifacts?

Kazuma Iwamoto, Tsubasa Ochiai, Marc Delcroix +4

Jointly training a speech enhancement (SE) front-end and an automatic speech recognition (ASR) back-end has been investigated as a way to mitigate the influence of \emph{processing…

eess.AS2023

Neural network-based virtual microphone estimation with virtual microphone and beamformer-level multi-task loss

Hanako Segawa, Tsubasa Ochiai, Marc Delcroix +5

Array processing performance depends on the number of microphones available. Virtual microphone estimation (VME) has been proposed to increase the number of microphone signals arti…

eess.AS2023

Modified Parametric Multichannel Wiener Filter \\for Low-latency Enhancement of Speech Mixtures with Unknown Number of Speakers

Ning Guo, Tomohiro Nakatani, Shoko Araki +1

This paper introduces a novel low-latency online beamforming (BF) algorithm, named Modified Parametric Multichannel Wiener Filter (Mod-PMWF), for enhancing speech mixtures with unk…

eess.AS2023

Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization

Marc Delcroix, Naohiro Tawara, Mireia Diez +6

Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods. EEND-VC esti…