Search Results for author:
Found 10 papers, 0 papers with code
A Pre-training Framework that Encodes Noise Information for Speech Quality Assessment
Self-supervised learning (SSL) has grown in interest within the speech processing community, since it produces representations that are useful for many downstream tasks.
A contrastive-learning approach for auditory attention detection
In this paper, we propose a method based on self supervised learning to minimize the difference between the latent representations of an attended speech signal and the corresponding EEG signal.
Using RLHF to align speech enhancement approaches to mean-opinion quality scores
Objective speech quality measures are typically used to assess speech enhancement algorithms, but it has been shown that they are sub-optimal as learning objectives because they do not always align well with human subjective ratings.
SWIM: An Attention-Only Model for Speech Quality Assessment Under Subjective Variance
However, variance in ratings between listeners can introduce noise in the true quality label of an utterance.
MMViT: Multiscale Multiview Vision Transformers
Our model encodes different views of the input signal and builds several channel-resolution feature stages to process the multiple views of the input at different resolutions in parallel.
Attention-based Speech Enhancement Using Human Quality Perception Modelling
In this work, we propose an attention-based enhancement approach that uses learned speech embedding vectors from a mean-opinion score (MOS) prediction model and a speech enhancement module to jointly enhance noisy speech.
A Composite T60 Regression and Classification Approach for Speech Dereverberation
More specifically, we develop a joint learning approach that uses a composite T60 module and a separate dereverberation module to simultaneously perform reverberation time estimation and dereverberation.
Multi-channel Multi-frame ADL-MVDR for Target Speech Separation
Many purely neural network based speech separation approaches have been proposed to improve objective assessment scores, but they often introduce nonlinear distortions that are harmful to modern automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Pyramid Recurrent Network for Predicting Crowdsourced Speech-Quality Ratings of Real-World Signals
The real-world capabilities of objective speech quality measures are limited since current measures (1) are developed from simulated data that does not adequately model real environments; or they (2) predict objective scores that are not always strongly correlated with subjective ratings.
On Loss Functions and Recurrency Training for GAN-based Speech Enhancement Systems
Recent work has shown that it is feasible to use generative adversarial networks (GANs) for speech enhancement, however, these approaches have not been compared to state-of-the-art (SOTA) non GAN-based approaches.
Audio and Speech Processing Sound
