120 citations · 519 across the 41 of their papers we have counts for
42 papers
Neural Target Speech Extraction: An Overview
Katerina Zmolikova, Marc Delcroix, Tsubasa Ochiai +3
Humans can listen to a target speaker even in challenging acoustic conditions that have noise, reverberation, and interfering speakers. This phenomenon is known as the cocktail-par…
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…
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…
Strategies to Improve Robustness of Target Speech Extraction to Enrollment Variations
Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix +6
Target speech extraction is a technique to extract the target speaker's voice from mixture signals using a pre-recorded enrollment utterance that characterize the voice characteris…
Listen only to me! How well can target speech extraction handle false alarms?
Marc Delcroix, Keisuke Kinoshita, Tsubasa Ochiai +3
Target speech extraction (TSE) extracts the speech of a target speaker in a mixture given auxiliary clues characterizing the speaker, such as an enrollment utterance. TSE addresses…
SoundBeam: Target sound extraction conditioned on sound-class labels and enrollment clues for increased performance and continuous learning
Marc Delcroix, Jorge Bennasar Vázquez, Tsubasa Ochiai +3
In many situations, we would like to hear desired sound events (SEs) while being able to ignore interference. Target sound extraction (TSE) tackles this problem by estimating the a…
Effective data screening technique for crowdsourced speech intelligibility experiments: Evaluation with IRM-based speech enhancement
Ayako Yamamoto, Toshio Irino, Shoko Araki +4
It is essential to perform speech intelligibility (SI) experiments with human listeners in order to evaluate objective intelligibility measures for developing effective speech enha…
Tight integration of neural- and clustering-based diarization through deep unfolding of infinite Gaussian mixture model
Keisuke Kinoshita, Marc Delcroix, Tomoharu Iwata
Speaker diarization has been investigated extensively as an important central task for meeting analysis. Recent trend shows that integration of end-to-end neural (EEND)-and cluster…
Learning to Enhance or Not: Neural Network-Based Switching of Enhanced and Observed Signals for Overlapping Speech Recognition
Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix +3
The combination of a deep neural network (DNN) -based speech enhancement (SE) front-end and an automatic speech recognition (ASR) back-end is a widely used approach to implement ov…
Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms
Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita +3
This paper develops a framework that can perform denoising, dereverberation, and source separation accurately by using a relatively small number of microphones. It has been empiric…
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,…
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation
Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita +2
This paper proposes an approach for optimizing a Convolutional BeamFormer (CBF) that can jointly perform denoising (DN), dereverberation (DR), and source separation (SS). First, we…
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…
Few-shot learning of new sound classes for target sound extraction
Marc Delcroix, Jorge Bennasar Vázquez, Tsubasa Ochiai +2
Target sound extraction consists of extracting the sound of a target acoustic event (AE) class from a mixture of AE sounds. It can be realized using a neural network that extracts…
PILOT: Introducing Transformers for Probabilistic Sound Event Localization
Christopher Schymura, Benedikt Bönninghoff, Tsubasa Ochiai +5
Sound event localization aims at estimating the positions of sound sources in the environment with respect to an acoustic receiver (e.g. a microphone array). Recent advances in thi…
Should We Always Separate?: Switching Between Enhanced and Observed Signals for Overlapping Speech Recognition
Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix +3
Although recent advances in deep learning technology improved automatic speech recognition (ASR), it remains difficult to recognize speech when it overlaps other people's voices. S…
Advances in integration of end-to-end neural and clustering-based diarization for real conversational speech
Keisuke Kinoshita, Marc Delcroix, Naohiro Tawara
Recently, we proposed a novel speaker diarization method called End-to-End-Neural-Diarization-vector clustering (EEND-vector clustering) that integrates clustering-based and end-to…
Comparison of remote experiments using crowdsourcing and laboratory experiments on speech intelligibility
Ayako Yamamoto, Toshio Irino, Kenichi Arai +4
Many subjective experiments have been performed to develop objective speech intelligibility measures, but the novel coronavirus outbreak has made it very difficult to conduct exper…
Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization
Christopher Schymura, Tsubasa Ochiai, Marc Delcroix +4
Sound event localization frameworks based on deep neural networks have shown increased robustness with respect to reverberation and noise in comparison to classical parametric appr…
Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain
Julio Wissing, Benedikt Boenninghoff, Dorothea Kolossa +6
Estimating the positions of multiple speakers can be helpful for tasks like automatic speech recognition or speaker diarization. Both applications benefit from a known speaker posi…
Dual-Path Modeling for Long Recording Speech Separation in Meetings
Chenda Li, Zhuo Chen, Yi Luo +6
The continuous speech separation (CSS) is a task to separate the speech sources from a long, partially overlapped recording, which involves a varying number of speakers. A straight…
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…
Speaker activity driven neural speech extraction
Marc Delcroix, Katerina Zmolikova, Tsubasa Ochiai +2
Target speech extraction, which extracts the speech of a target speaker in a mixture given auxiliary speaker clues, has recently received increased interest. Various clues have bee…