1 citations · 1 across the 16 of their papers we have counts for
19 papers
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…
Improving Predicted MOS Scores, Not Perceived Quality: Multi-Predictor Test-Time Optimization of Enhanced Speech
Tsubasa Ochiai, Marc Delcroix, Nahomi Kusunoki +5
Non-intrusive MOS predictors are widely used instead of subjective listening tests to evaluate and rank speech enhancement (SE) systems. If they accurately reflect perceived qualit…
Ontology-based Target Sound Extraction
Carlos Hernandez-Olivan, Marc Delcroix, Tsubasa Ochiai +2
Target sound extraction (TSE) aims to isolate a sound source of interest from a mixture, given a semantic query. Existing TSE systems are conditioned on fixed class representations…
Generating Training Targets for Real-World Speech Enhancement via Close-to-Distant Microphone Projection
Tomohiro Nakatani, Rintaro Ikeshita, Naoyuki Kamo +2
Training neural networks (NNs) for speech enhancement (SE) in distant speech-capturing scenarios requires paired distorted and clean reference speech signals. While such data are o…
Description and Discussion on DCASE 2026 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes
Binh Thien Nguyen, Masahiro Yasuda, Noboru Harada +8
This paper presents an overview of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2026 Challenge Task 4, Spatial Semantic Segmentation of Sound Scenes (S5).…
Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits
Gilad Nurko, Roi Benita, Yehoshua Dissen +4
Robust classification in noisy environments remains a fundamental challenge in machine learning. Standard approaches typically treat signal enhancement and classification as separa…
Reference Microphone Selection for Guided Source Separation based on the Normalized L-p Norm
Anselm Lohmann, Tomohiro Nakatani, Rintaro Ikeshita +3
Guided Source Separation (GSS) is a popular front-end for distant automatic speech recognition (ASR) systems using spatially distributed microphones. When considering spatially dis…
Dissecting the Segmentation Model of End-to-End Diarization with Vector Clustering
Alexis Plaquet, Naohiro Tawara, Marc Delcroix +4
End-to-End Neural Diarization with Vector Clustering is a powerful and practical approach to perform Speaker Diarization. Multiple enhancements have been proposed for the segmentat…
Description and Discussion on DCASE 2025 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes
Masahiro Yasuda, Binh Thien Nguyen, Noboru Harada +10
Spatial Semantic Segmentation of Sound Scenes (S5) aims to enhance technologies for sound event detection and separation from multi-channel input signals that mix multiple sound ev…
TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models
Junyi Peng, Takanori Ashihara, Marc Delcroix +4
Self-supervised learning (SSL) models have significantly advanced speech processing tasks, and several benchmarks have been proposed to validate their effectiveness. However, previ…
Microphone Array Geometry Independent Multi-Talker Distant ASR: NTT System for the DASR Task of the CHiME-8 Challenge
Naoyuki Kamo, Naohiro Tawara, Atsushi Ando +15
In this paper, we introduce a multi-talker distant automatic speech recognition (DASR) system we designed for the DASR task 1 of the CHiME-8 challenge. Our system performs speaker…
30+ Years of Source Separation Research: Achievements and Future Challenges
Shoko Araki, Nobutaka Ito, Reinhold Haeb-Umbach +3
Source separation (SS) of acoustic signals is a research field that emerged in the mid-1990s and has flourished ever since. On the occasion of ICASSP's 50th anniversary, we review…
Mamba-based Segmentation Model for Speaker Diarization
Alexis Plaquet, Naohiro Tawara, Marc Delcroix +3
Mamba is a newly proposed architecture which behaves like a recurrent neural network (RNN) with attention-like capabilities. These properties are promising for speaker diarization,…
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…
NTT Multi-Speaker ASR System for the DASR Task of CHiME-8 Challenge
Naoyuki Kamo, Naohiro Tawara, Atsushi Ando +15
We present a distant automatic speech recognition (DASR) system developed for the CHiME-8 DASR track. It consists of a diarization first pipeline. For diarization, we use end-to-en…
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…
Rethinking Processing Distortions: Disentangling the Impact of Speech Enhancement Errors on Speech Recognition Performance
Tsubasa Ochiai, Kazuma Iwamoto, Marc Delcroix +4
It is challenging to improve automatic speech recognition (ASR) performance in noisy conditions with a single-channel speech enhancement (SE) front-end. This is generally attribute…
Probing Self-supervised Learning Models with Target Speech Extraction
Junyi Peng, Marc Delcroix, Tsubasa Ochiai +4
Large-scale pre-trained self-supervised learning (SSL) models have shown remarkable advancements in speech-related tasks. However, the utilization of these models in complex multi-…
Target Speech Extraction with Pre-trained Self-supervised Learning Models
Junyi Peng, Marc Delcroix, Tsubasa Ochiai +3
Pre-trained self-supervised learning (SSL) models have achieved remarkable success in various speech tasks. However, their potential in target speech extraction (TSE) has not been…