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Found 6 papers, 1 papers with code
Neural Spatio-Temporal Beamformer for Target Speech Separation
Purely neural network (NN) based speech separation and enhancement methods, although can achieve good objective scores, inevitably cause nonlinear speech distortions that are harmful for the automatic speech recognition (ASR).
Audio and Speech Processing Sound
Multi-modal Multi-channel Target Speech Separation
Target speech separation refers to extracting a target speaker's voice from an overlapped audio of simultaneous talkers.
Enhancing End-to-End Multi-channel Speech Separation via Spatial Feature Learning
Hand-crafted spatial features (e. g., inter-channel phase difference, IPD) play a fundamental role in recent deep learning based multi-channel speech separation (MCSS) methods.
Improving Reverberant Speech Training Using Diffuse Acoustic Simulation
We present an efficient and realistic geometric acoustic simulation approach for generating and augmenting training data in speech-related machine learning tasks.
End-to-End Multi-Channel Speech Separation
This paper extended the previous approach and proposed a new end-to-end model for multi-channel speech separation.
Time Domain Audio Visual Speech Separation
Audio-visual multi-modal modeling has been demonstrated to be effective in many speech related tasks, such as speech recognition and speech enhancement.
Audio and Speech Processing Sound
