A hearing device, e.g. a hearing aid, configured to be worn by a user comprising • A multitude of input units, each providing an electric input signal (X 1 ( k, l '),..., X M ( k, l ')) representing sound in the environment of the user in a time-frequency representation ( k, l), wherein the sound is a mixture of speech and additive noise or other distortions, e.g. reverberation, • A multitude of beamformer filtering units, each being configured to receive at least two, e.g. all, of said multitude of electric input signals (X 1 ( k,l'),..., X M (k,l')), each of said multitude of beamformer filtering units being configured to provide a beamformed signal (X(k,l',θ i,r j)) representative of the sound in a different one of a multitude of spatial segments (θ i,r j), e.g. spatial cells, around the user, • a multitude of speech probability estimators (DNN Ψ ∗ θ|,rj) each configured to receive the beamformed signal (X(k,l',θ i,r j)) for a particular spatial segment (θ i,r j) and to estimate a probability that said particular spatial segment (θ |,r j) contains speech at a given point in time and frequency; • CHARACTERIZED IN THAT said hearing device comprises a resulting beamformer filtering unit configured to implement a resulting beamformer ( W res (k,l',θ i,r j)), which is a linear combination of beamformers ( W x (k,l',θ i,r j)) directed at each spatial segment (θ i,r j) provided by said multitude of beamformer filtering units, where the coefficients ( w res (k,l')) of the linear combination are derived from the speech presence probabilities ((P ij (k,l)) and beamformer weights( w ij ( k, l')) for providing the multitude of beamformed signals (X(k,l',θ i,r j)), and where said resulting beamformer filtering unit is configured to provide a resulting beamformed signal (Y res (k,l')), and wherein • at least one, e.g. all, of the multitude of speech probability estimators (DNN Ψ ∗ θ|,rj) is/are implemented as a trained neural network, e.g. a deep neural network. • A multitude of input units, each providing an electric input signal (X 1 ( k, l '),..., X M ( k, l ')) representing sound in the environment of the user in a time-frequency representation ( k, l), wherein the sound is a mixture of speech and additive noise or other distortions, e.g. reverberation, • A multitude of beamformer filtering units, each being configured to receive at least two, e.g. all, of said multitude of electric input signals (X 1 ( k,l'),..., X M (k,l')), each of said multitude of beamformer filtering units being configured to provide a beamformed signal (X(k,l',θ i,r j)) representative of the sound in a different one of a multitude of spatial segments (θ i,r j), e.g. spatial cells, around the user, • a multitude of speech probability estimators (DNN Ψ ∗ θ|,rj) each configured to receive the beamformed signal (X(k,l',θ i,r j)) for a particular spatial segment (θ i,r j) and to estimate a probability that said particular spatial segment (θ |,r j) contains speech at a given point in time and frequency; • CHARACTERIZED IN THAT said hearing device comprises a resulting beamformer filtering unit configured to implement a resulting beamformer ( W res (k,l',θ i,r j)), which is a linear combination of beamformers ( W x (k,l',θ i,r j)) directed at each spatial segment (θ i,r j) provided by said multitude of beamformer filtering units, where the coefficients ( w res (k,l')) of the linear combination are derived from the speech presence probabilities ((P ij (k,l)) and beamformer weights( w ij ( k, l')) for providing the multitude of beamformed signals (X(k,l',θ i,r j)), and where said resulting beamformer filtering unit is configured to provide a resulting beamformed signal (Y res (k,l')), and wherein • at least one, e.g. all, of the multitude of speech probability estimators (DNN Ψ ∗ θ|,rj) is/are implemented as a trained neural network, e.g. a deep neural network.