A speech enhancement system, comprising: one or more processors; and memory comprising instructions executable by the one or more processors to implement: a spatio-temporal residual network configured to receive video frames from a video containing a target speaker and extract visual features from the video frames; an autoencoder comprising an encoder, a decoder, and skip connections between the encoder and the decoder, the autoencoder configured to receive input of an audio spectrogram, the audio spectrogram obtained based upon an audio waveform extracted from the video containing the target speaker, the autoencoder being configured to extract audio features from the audio spectrogram; and a squeeze-excitation fusion block configured to: receive input of a visual feature F v from a last layer of the spatio-temporal residual network and input of audio features F a from all layers of the autoencoder, apply global average pooling to the audio features and to the visual feature to compress the audio features over a channel dimension to obtain audio feature representations F a ', and to compress the visual feature over a spatial dimension to obtain a visual feature representation F v '; apply a 1D convolution across a frequency dimension of an audio feature representation resulting in F a ", and apply a 1D convolution across a channel dimension of the audio feature representation, resulting in F v "; reshape and concatenate F a " and F v " into a fused feature vector F av; apply a linear transformation h and a rectified linear unit (ReLU) activation to the fused feature vector, such that M = ReLU(h(F av)); apply a second linear transformation g, such that G = σ ( g), where σ is sigmoid activation; broadcast G to the dimension of F a and apply gating over a time dimension of the audio features as F a j + 1 = G ⊙ F a j; and provide an output to the decoder of the autoencoder; wherein the decoder is configured to output a mask based upon the fusion of audio features and visual features by the squeeze-excitation fusion block, and wherein the instructions are executable to apply the mask to the audio spectrogram to generate an enhanced magnitude spectrogram, and to reconstruct an enhanced waveform from the enhanced magnitude spectrogram. one or more processors; and memory comprising instructions executable by the one or more processors to implement: a spatio-temporal residual network configured to receive video frames from a video containing a target speaker and extract visual features from the video frames; an autoencoder comprising an encoder, a decoder, and skip connections between the encoder and the decoder, the autoencoder configured to receive input of an audio spectrogram, the audio spectrogram obtained based upon an audio waveform extracted from the video containing the target speaker, the autoencoder being configured to extract audio features from the audio spectrogram; and a spatio-temporal residual network configured to receive video frames from a video containing a target speaker and extract visual features from the video frames; an autoencoder comprising an encoder, a decoder, and skip connections between the encoder and the decoder, the autoencoder configured to receive input of an audio spectrogram, the audio spectrogram obtained based upon an audio waveform extracted from the video containing the target speaker, the autoencoder being configured to extract audio features from the audio spectrogram; and receive input of a visual feature F v from a last layer of the spatio-temporal residual network and input of audio features F a from all layers of the autoencoder, apply global average pooling to the audio features and to the visual feature to compress the audio features over a channel dimension to obtain audio feature representations F a ', and to compress the visual feature over a spatial dimension to obtain a visual feature representation F v '; apply a 1D convolution across a frequency dimension of an audio feature representation resulting in F a ", and apply a 1D convolution across a channel dimension of the audio feature representation, resulting in F v "; reshape and concatenate F a " and F v " into a fused feature vector F av; apply a linear transformation h and a rectified linear unit (ReLU) activation to the fused feature vector, such that M = ReLU(h(F av)); apply a second linear transformation g, such that G = σ ( g), where σ is sigmoid activation; broadcast G to the dimension of F a and apply gating over a time dimension of the audio features as F a j + 1 = G ⊙ F a j; and provide an output to the decoder of the autoencoder; wherein the decoder is configured to output a mask based upon the fusion of audio features and visual features by the squeeze-excitation fusion block, and wherein the instructions are executable to apply the mask to the audio spectrogram to generate an enhanced magnitude spectrogram, and to reconstruct an enhanced waveform from the enhanced magnitude spectrogram.