56 citations · 344 across the 33 of their papers we have counts for
24 papers · 1 filter
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…
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…
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…
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…
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration
Chenda Li, Jing Shi, Wangyou Zhang +8
We present ESPnet-SE, which is designed for the quick development of speech enhancement and speech separation systems in a single framework, along with the optional downstream spee…
Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR
Thilo von Neumann, Christoph Boeddeker, Lukas Drude +4
Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is t…
Jointly optimal denoising, dereverberation, and source separation
Tomohiro Nakatani, Christoph Boeddeker, Keisuke Kinoshita +3
This paper proposes methods that can optimize a Convolutional BeamFormer (CBF) for jointly performing denoising, dereverberation, and source separation (DN+DR+SS) in a computationa…
End-to-end training of time domain audio separation and recognition
Thilo von Neumann, Keisuke Kinoshita, Lukas Drude +4
The rising interest in single-channel multi-speaker speech separation sparked development of End-to-End (E2E) approaches to multi-speaker speech recognition. However, up until now,…