Search Results for author:
Found 11 papers, 0 papers with code
Rethinking Processing Distortions: Disentangling the Impact of Speech Enhancement Errors on Speech Recognition Performance
To this end, we propose a novel analysis scheme based on the orthogonal projection-based decomposition of SE errors.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
How does end-to-end speech recognition training impact speech enhancement artifacts?
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 distortion} generated by single-channel SE on ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Neural network-based virtual microphone estimation with virtual microphone and beamformer-level multi-task loss
However, this training objective may not be optimal for a specific array processing back-end, such as beamforming.
ISS2: An Extension of Iterative Source Steering Algorithm for Majorization-Minimization-Based Independent Vector Analysis
Although the time complexity per iteration of ISS is $m$ times smaller than that of IP, the conventional ISS converges slower than the current fastest IP (called $\text{IP}_2$) that updates two rows of $W$ in each iteration.
How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR
The artifact component is defined as the SE error signal that cannot be represented as a linear combination of speech and noise sources.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms
This paper develops a framework that can perform denoising, dereverberation, and source separation accurately by using a relatively small number of microphones.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation
This paper proposes an approach for optimizing a Convolutional BeamFormer (CBF) that can jointly perform denoising (DN), dereverberation (DR), and source separation (SS).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Independent Vector Extraction for Fast Joint Blind Source Separation and Dereverberation
We address a blind source separation (BSS) problem in a noisy reverberant environment in which the number of microphones $M$ is greater than the number of sources of interest, and the other noise components can be approximated as stationary and Gaussian distributed.
A Joint Diagonalization Based Efficient Approach to Underdetermined Blind Audio Source Separation Using the Multichannel Wiener Filter
Based on this approach, we present FastFCA, a computationally efficient extension of FCA.
Audio Source Separation
Sound
Audio and Speech Processing
Neural Network-based Virtual Microphone Estimator
Developing microphone array technologies for a small number of microphones is important due to the constraints of many devices.
Block Coordinate Descent Algorithms for Auxiliary-Function-Based Independent Vector Extraction
We also newly develop a BCD for a semiblind IVE in which the transfer functions for several super-Gaussian sources are given a priori.
