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Found 9 papers, 0 papers with code
A Single Speech Enhancement Model Unifying Dereverberation, Denoising, Speaker Counting, Separation, and Extraction
We propose a multi-task universal speech enhancement (MUSE) model that can perform five speech enhancement (SE) tasks: dereverberation, denoising, speech separation (SS), target speaker extraction (TSE), and speaker counting.
Toward Universal Speech Enhancement for Diverse Input Conditions
Currently, there is no universal SE approach that can effectively handle diverse input conditions with a single model.
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study
Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling.
Exploring Speech Enhancement for Low-resource Speech Synthesis
High-quality and intelligible speech is essential to text-to-speech (TTS) model training, however, obtaining high-quality data for low-resource languages is challenging and expensive.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Remixing-based Unsupervised Source Separation from Scratch
A student model is then trained to separate the pseudo-mixtures using either the teacher's outputs or the initial mixtures as supervision.
Self-Remixing: Unsupervised Speech Separation via Separation and Remixing
Specifically, the shuffler first separates observed mixtures and makes pseudo-mixtures by shuffling and remixing the separated signals.
Spatial Loss for Unsupervised Multi-channel Source Separation
With the proposed loss, we train the neural separators based on minimum variance distortionless response (MVDR) beamforming and independent vector analysis (IVA).
Remix-cycle-consistent Learning on Adversarially Learned Separator for Accurate and Stable Unsupervised Speech Separation
A new learning algorithm for speech separation networks is designed to explicitly reduce residual noise and artifacts in the separated signal in an unsupervised manner.
Independence-based Joint Dereverberation and Separation with Neural Source Model
We introduce a neural network in the framework of time-decorrelation iterative source steering, which is an extension of independent vector analysis to joint dereverberation and separation.
