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Audio-Visual Speech Inpainting with Deep Learning
Giovanni Morrone, Daniel Michelsanti, Zheng-Hua Tan +1
In this paper, we present a deep-learning-based framework for audio-visual speech inpainting, i.e., the task of restoring the missing parts of an acoustic speech signal from reliab…
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Daniel Michelsanti, Zheng-Hua Tan, Shi-Xiong Zhang +4
Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generat…
Exploring Filterbank Learning for Keyword Spotting
Iván López-Espejo, Zheng-Hua Tan, Jesper Jensen
Despite their great performance over the years, handcrafted speech features are not necessarily optimal for any particular speech application. Consequently, with greater or lesser…
Vocoder-Based Speech Synthesis from Silent Videos
Daniel Michelsanti, Olga Slizovskaia, Gloria Haro +3
Both acoustic and visual information influence human perception of speech. For this reason, the lack of audio in a video sequence determines an extremely low speech intelligibility…
Deep-Learning-Based Audio-Visual Speech Enhancement in Presence of Lombard Effect
Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson +1
When speaking in presence of background noise, humans reflexively change their way of speaking in order to improve the intelligibility of their speech. This reflex is known as Lomb…
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…