45 citations · 175 across the 20 of their papers we have counts for
22 papers
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…
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…
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…
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…
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…
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…
How does end-to-end speech recognition training impact speech enhancement artifacts?
Kazuma Iwamoto, Tsubasa Ochiai, Marc Delcroix +4
Jointly training a speech enhancement (SE) front-end and an automatic speech recognition (ASR) back-end has been investigated as a way to mitigate the influence of \emph{processing…
Neural network-based virtual microphone estimation with virtual microphone and beamformer-level multi-task loss
Hanako Segawa, Tsubasa Ochiai, Marc Delcroix +5
Array processing performance depends on the number of microphones available. Virtual microphone estimation (VME) has been proposed to increase the number of microphone signals arti…
NoisyILRMA: Diffuse-Noise-Aware Independent Low-Rank Matrix Analysis for Fast Blind Source Extraction
Koki Nishida, Norihiro Takamune, Rintaro Ikeshita +3
In this paper, we address the multichannel blind source extraction (BSE) of a single source in diffuse noise environments. To solve this problem even faster than by fast multichann…
ISS2: An Extension of Iterative Source Steering Algorithm for Majorization-Minimization-Based Independent Vector Analysis
Rintaro Ikeshita, Tomohiro Nakatani
A majorization-minimization (MM) algorithm for independent vector analysis optimizes a separation matrix by minimizing a surr…
How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR
Kazuma Iwamoto, Tsubasa Ochiai, Marc Delcroix +4
It is challenging to improve automatic speech recognition (ASR) performance in noisy conditions with single-channel speech enhancement (SE). In this paper, we investigate the cause…
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…
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…
Independent Deeply Learned Tensor Analysis for Determined Audio Source Separation
Naoki Narisawa, Rintaro Ikeshita, Norihiro Takamune +4
We address the determined audio source separation problem in the time-frequency domain. In independent deeply learned matrix analysis (IDLMA), it is assumed that the inter-frequenc…
Independent Vector Extraction for Fast Joint Blind Source Separation and Dereverberation
Rintaro Ikeshita, Tomohiro Nakatani
We address a blind source separation (BSS) problem in a noisy reverberant environment in which the number of microphones is greater than the number of sources of interest, and…
A Joint Diagonalization Based Efficient Approach to Underdetermined Blind Audio Source Separation Using the Multichannel Wiener Filter
Nobutaka Ito, Rintaro Ikeshita, Hiroshi Sawada +1
This paper presents a computationally efficient approach to blind source separation (BSS) of audio signals, applicable even when there are more sources than microphones (i.e., the…
Neural Network-based Virtual Microphone Estimator
Tsubasa Ochiai, Marc Delcroix, Tomohiro Nakatani +3
Developing microphone array technologies for a small number of microphones is important due to the constraints of many devices. One direction to address this situation consists of…
Block Coordinate Descent Algorithms for Auxiliary-Function-Based Independent Vector Extraction
Rintaro Ikeshita, Tomohiro Nakatani, Shoko Araki
In this paper, we address the problem of extracting all super-Gaussian source signals from a linear mixture in which (i) the number of super-Gaussian sources is less than that…
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…
Overdetermined independent vector analysis
Rintaro Ikeshita, Tomohiro Nakatani, Shoko Araki
We address the convolutive blind source separation problem for the (over-)determined case where (i) the number of nonstationary target-sources is less than that of microphones…
Convergence-guaranteed Independent Positive Semidefinite Tensor Analysis Based on Student's t Distribution
Tatsuki Kondo, Kanta Fukushige, Norihiro Takamune +4
In this paper, we address a blind source separation (BSS) problem and propose a new extended framework of independent positive semidefinite tensor analysis (IPSDTA). IPSDTA is a st…
Count Matroids of Group-Labeled Graphs
Rintaro Ikeshita, Shin-ichi Tanigawa
A graph is called -sparse if for any nonempty , where denotes the set of vertices incident to . It is known that…