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eess.AS2018★ 5 cited
Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems
Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson +1
Humans tend to change their way of speaking when they are immersed in a noisy environment, a reflex known as Lombard effect. Current speech enhancement systems based on deep learni…
eess.AS2018★ 17 cited
On Training Targets and Objective Functions for Deep-Learning-Based Audio-Visual Speech Enhancement
Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson +1
Audio-visual speech enhancement (AV-SE) is the task of improving speech quality and intelligibility in a noisy environment using audio and visual information from a talker. Recentl…
cs.SD2018★ 24 cited
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…