120 citations · 452 across the 91 of their papers we have counts for
14 papers · 1 filter
Continuous Speech Separation Using Speaker Inventory for Long Multi-talker Recording
Cong Han, Yi Luo, Chenda Li +8
Leveraging additional speaker information to facilitate speech separation has received increasing attention in recent years. Recent research includes extracting target speech by us…
Integration of variational autoencoder and spatial clustering for adaptive multi-channel neural speech separation
Katerina Zmolikova, Marc Delcroix, Lukáš Burget +2
In this paper, we propose a method combining variational autoencoder model of speech with a spatial clustering approach for multi-channel speech separation. The advantage of integr…
Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation
Christoph Boeddeker, Wangyou Zhang, Tomohiro Nakatani +6
Time-domain training criteria have proven to be very effective for the separation of single-channel non-reverberant speech mixtures. Likewise, mask-based beamforming has shown impr…
Integrating end-to-end neural and clustering-based diarization: Getting the best of both worlds
Keisuke Kinoshita, Marc Delcroix, Naohiro Tawara
Recent diarization technologies can be categorized into two approaches, i.e., clustering and end-to-end neural approaches, which have different pros and cons. The clustering-based…
Far-Field Automatic Speech Recognition
Reinhold Haeb-Umbach, Jahn Heymann, Lukas Drude +3
The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic speech recognition (ASR), has received a significant increase of attention…
Multi-path RNN for hierarchical modeling of long sequential data and its application to speaker stream separation
Keisuke Kinoshita, Thilo von Neumann, Marc Delcroix +2
Recently, the source separation performance was greatly improved by time-domain audio source separation based on dual-path recurrent neural network (DPRNN). DPRNN is a simple but e…
Listen to What You Want: Neural Network-based Universal Sound Selector
Tsubasa Ochiai, Marc Delcroix, Yuma Koizumi +3
Being able to control the acoustic events (AEs) to which we want to listen would allow the development of more controllable hearable devices. This paper addresses the AE sound sele…
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…
Cognitive-driven convolutional beamforming using EEG-based auditory attention decoding
Ali Aroudi, Marc Delcroix, Tomohiro Nakatani +3
The performance of speech enhancement algorithms in a multi-speaker scenario depends on correctly identifying the target speaker to be enhanced. Auditory attention decoding (AAD) m…
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…
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network
Keisuke Kinoshita, Tsubasa Ochiai, Marc Delcroix +1
With the advent of deep learning, research on noise-robust automatic speech recognition (ASR) has progressed rapidly. However, ASR performance in noisy conditions of single-channel…
Tackling real noisy reverberant meetings with all-neural source separation, counting, and diarization system
Keisuke Kinoshita, Marc Delcroix, Shoko Araki +1
Automatic meeting analysis is an essential fundamental technology required to let, e.g. smart devices follow and respond to our conversations. To achieve an optimal automatic meeti…
Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention
Yuma Koizumi, Kohei Yatabe, Marc Delcroix +2
This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly fro…
Improving speaker discrimination of target speech extraction with time-domain SpeakerBeam
Marc Delcroix, Tsubasa Ochiai, Katerina Zmolikova +4
Target speech extraction, which extracts a single target source in a mixture given clues about the target speaker, has attracted increasing attention. We have recently proposed Spe…