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On TasNet for Low-Latency Single-Speaker Speech Enhancement
Morten Kolbæk, Zheng-Hua Tan, Søren Holdt Jensen +1
In recent years, speech processing algorithms have seen tremendous progress primarily due to the deep learning renaissance. This is especially true for speech separation where the…
On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement
Morten Kolbæk, Zheng-Hua Tan, Søren Holdt Jensen +1
Many deep learning-based speech enhancement algorithms are designed to minimize the mean-square error (MSE) in some transform domain between a predicted and a target speech signal.…
Keyword Spotting for Hearing Assistive Devices Robust to External Speakers
Iván López-Espejo, Zheng-Hua Tan, Jesper Jensen
Keyword spotting (KWS) is experiencing an upswing due to the pervasiveness of small electronic devices that allow interaction with them via speech. Often, KWS systems are speaker-i…
On the Relationship Between Short-Time Objective Intelligibility and Short-Time Spectral-Amplitude Mean-Square Error for Speech Enhancement
Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen
The majority of deep neural network (DNN) based speech enhancement algorithms rely on the mean-square error (MSE) criterion of short-time spectral amplitudes (STSA), which has no a…
Monaural Speech Enhancement using Deep Neural Networks by Maximizing a Short-Time Objective Intelligibility Measure
Morten Kolbæk, Zheng-Hua Tan, Jesper Jensen
In this paper we propose a Deep Neural Network (DNN) based Speech Enhancement (SE) system that is designed to maximize an approximation of the Short-Time Objective Intelligibility…