120 citations · 347 across the 47 of their papers we have counts for
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How Reliable Are Predicted MOS for Reproducing Human System-Level Preferences in Speech Enhancement?
Nahomi Kusunoki, Tsubasa Ochiai, Naohiro Tawara +4
We investigate whether predicted Mean Opinion Scores (MOS) can reliably support system-level comparisons of speech enhancement (SE) methods by introducing system-level preference a…
Frontend Token Enhancement for Token-Based Speech Recognition
Takanori Ashihara, Shota Horiguchi, Kohei Matsuura +2
Discretized representations of speech signals are efficient alternatives to continuous features for various speech applications, including automatic speech recognition (ASR) and sp…
Investigation of Speaker Representation for Target-Speaker Speech Processing
Takanori Ashihara, Takafumi Moriya, Shota Horiguchi +5
Target-speaker speech processing (TS) tasks, such as target-speaker automatic speech recognition (TS-ASR), target speech extraction (TSE), and personal voice activity detection (p-…
SoundBeam meets M2D: Target Sound Extraction with Audio Foundation Model
Carlos Hernandez-Olivan, Marc Delcroix, Tsubasa Ochiai +4
Target sound extraction (TSE) consists of isolating a desired sound from a mixture of arbitrary sounds using clues to identify it. A TSE system requires solving two problems at onc…
Interaural time difference loss for binaural target sound extraction
Carlos Hernandez-Olivan, Marc Delcroix, Tsubasa Ochiai +3
Binaural target sound extraction (TSE) aims to extract a desired sound from a binaural mixture of arbitrary sounds while preserving the spatial cues of the desired sound. Indeed, f…
Analysis of impact of emotions on target speech extraction and speech separation
Ján Švec, Kateřina Žmolíková, Martin Kocour +4
Recently, the performance of blind speech separation (BSS) and target speech extraction (TSE) has greatly progressed. Most works, however, focus on relatively well-controlled condi…
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…
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…
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…
Multichannel End-to-end Speech Recognition
Tsubasa Ochiai, Shinji Watanabe, Takaaki Hori +1
The field of speech recognition is in the midst of a paradigm shift: end-to-end neural networks are challenging the dominance of hidden Markov models as a core technology. Using an…