56 citations · 344 across the 33 of their papers we have counts for
35 papers
Microphone Array Signal Processing and Deep Learning for Speech Enhancement
Reinhold Haeb-Umbach, Tomohiro Nakatani, Marc Delcroix +2
Multi-channel acoustic signal processing is a well-established and powerful tool to exploit the spatial diversity between a target signal and non-target or noise sources for signal…
Simultaneous Diarization and Separation of Meetings through the Integration of Statistical Mixture Models
Tobias Cord-Landwehr, Christoph Boeddeker, Reinhold Haeb-Umbach
We propose an approach for simultaneous diarization and separation of meeting data. It consists of a complex Angular Central Gaussian Mixture Model (cACGMM) for speech source separ…
The CHiME-8 DASR Challenge for Generalizable and Array Agnostic Distant Automatic Speech Recognition and Diarization
Samuele Cornell, Taejin Park, Steve Huang +6
This paper presents the CHiME-8 DASR challenge which carries on from the previous edition CHiME-7 DASR (C7DASR) and the past CHiME-6 challenge. It focuses on joint multi-channel di…
Once more Diarization: Improving meeting transcription systems through segment-level speaker reassignment
Christoph Boeddeker, Tobias Cord-Landwehr, Reinhold Haeb-Umbach
Diarization is a crucial component in meeting transcription systems to ease the challenges of speech enhancement and attribute the transcriptions to the correct speaker. Particular…
Geodesic interpolation of frame-wise speaker embeddings for the diarization of meeting scenarios
Tobias Cord-Landwehr, Christoph Boeddeker, Cătălin Zorilă +2
We propose a modified teacher-student training for the extraction of frame-wise speaker embeddings that allows for an effective diarization of meeting scenarios containing partiall…
Meeting Recognition with Continuous Speech Separation and Transcription-Supported Diarization
Thilo von Neumann, Christoph Boeddeker, Tobias Cord-Landwehr +2
We propose a modular pipeline for the single-channel separation, recognition, and diarization of meeting-style recordings and evaluate it on the Libri-CSS dataset. Using a Continuo…
Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription
Peter Vieting, Simon Berger, Thilo von Neumann +3
Many real-life applications of automatic speech recognition (ASR) require processing of overlapped speech. A common method involves first separating the speech into overlap-free st…
MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems
Thilo von Neumann, Christoph Boeddeker, Marc Delcroix +1
MeetEval is an open-source toolkit to evaluate all kinds of meeting transcription systems. It provides a unified interface for the computation of commonly used Word Error Rates (WE…
Mixture Encoder for Joint Speech Separation and Recognition
Simon Berger, Peter Vieting, Christoph Boeddeker +2
Multi-speaker automatic speech recognition (ASR) is crucial for many real-world applications, but it requires dedicated modeling techniques. Existing approaches can be divided into…
A Teacher-Student approach for extracting informative speaker embeddings from speech mixtures
Tobias Cord-Landwehr, Christoph Boeddeker, Cătălin Zorilă +2
We introduce a monaural neural speaker embeddings extractor that computes an embedding for each speaker present in a speech mixture. To allow for supervised training, a teacher-stu…
Frame-wise and overlap-robust speaker embeddings for meeting diarization
Tobias Cord-Landwehr, Christoph Boeddeker, Cătălin Zorilă +2
Using a Teacher-Student training approach we developed a speaker embedding extraction system that outputs embeddings at frame rate. Given this high temporal resolution and the fact…
TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings
Christoph Boeddeker, Aswin Shanmugam Subramanian, Gordon Wichern +2
Since diarization and source separation of meeting data are closely related tasks, we here propose an approach to perform the two objectives jointly. It builds upon the target-spea…
Reverberation as Supervision for Speech Separation
Rohith Aralikatti, Christoph Boeddeker, Gordon Wichern +2
This paper proposes reverberation as supervision (RAS), a novel unsupervised loss function for single-channel reverberant speech separation. Prior methods for unsupervised separati…
On Word Error Rate Definitions and their Efficient Computation for Multi-Speaker Speech Recognition Systems
Thilo von Neumann, Christoph Boeddeker, Keisuke Kinoshita +2
We propose a general framework to compute the word error rate (WER) of ASR systems that process recordings containing multiple speakers at their input and that produce multiple out…
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…
Utterance-by-utterance overlap-aware neural diarization with Graph-PIT
Keisuke Kinoshita, Thilo von Neumann, Marc Delcroix +2
Recent speaker diarization studies showed that integration of end-to-end neural diarization (EEND) and clustering-based diarization is a promising approach for achieving state-of-t…
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…
An Initialization Scheme for Meeting Separation with Spatial Mixture Models
Christoph Boeddeker, Tobias Cord-Landwehr, Thilo von Neumann +1
Spatial mixture model (SMM) supported acoustic beamforming has been extensively used for the separation of simultaneously active speakers. However, it has hardly been considered fo…
Monaural source separation: From anechoic to reverberant environments
Tobias Cord-Landwehr, Christoph Boeddeker, Thilo von Neumann +3
Impressive progress in neural network-based single-channel speech source separation has been made in recent years. But those improvements have been mostly reported on anechoic data…
SA-SDR: A novel loss function for separation of meeting style data
Thilo von Neumann, Keisuke Kinoshita, Christoph Boeddeker +2
Many state-of-the-art neural network-based source separation systems use the averaged Signal-to-Distortion Ratio (SDR) as a training objective function. The basic SDR is, however,…
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…
Graph-PIT: Generalized permutation invariant training for continuous separation of arbitrary numbers of speakers
Thilo von Neumann, Keisuke Kinoshita, Christoph Boeddeker +2
Automatic transcription of meetings requires handling of overlapped speech, which calls for continuous speech separation (CSS) systems. The uPIT criterion was proposed for utteranc…
A Comparison and Combination of Unsupervised Blind Source Separation Techniques
Christoph Boeddeker, Frederik Rautenberg, Reinhold Haeb-Umbach
Unsupervised blind source separation methods do not require a training phase and thus cannot suffer from a train-test mismatch, which is a common concern in neural network based so…
End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend
Wangyou Zhang, Christoph Boeddeker, Shinji Watanabe +7
Recently, the end-to-end approach has been successfully applied to multi-speaker speech separation and recognition in both single-channel and multichannel conditions. However, seve…