Hearing device comprising a speech presence probability estimator

A hearing device configured to be worn by a user comprising:

• a multitude of input units, each providing an electric input signal representing sound in the environment of the user in a time-frequency representation, wherein the sound is a mixture of speech and additive noise or other distortions;
• a multitude of beamformer filtering units, each being configured to receive at least two of said multitude of electric input signals, each of said multitude of beamformer filtering units being configured to provide a beamformed signal representative of the sound in a different one of a multitude of spatial segments;
• a multitude of speech probability estimators each configured to receive the beamformed signal for a particular spatial segment and to estimate a probability that said particular spatial segment contains speech at a given point in time and frequency;
• and wherein
• at least one of the multitude of speech probability estimators is/are implemented as a trained neural network,
• wherein the at least one of the multitude of speech probability estimators is/are implemented as a trained neural network according to the method of claim 10.

A method of providing an estimate Î* of a speech presence probability in a sound signal comprising speech and additive noise or other distortions in a hearing device, the hearing device comprising a multitude of input units each providing an electric input signal representing said sound signal in a time-frequency representation (k, l), the method comprising:

• providing a subdivision of space around the user in a multitude of spatial segments (i,j);
• providing a speech presence indicator function, which for a given electric input signal indicates whether or not, or to which extent, speech is present in a given spatial segment (i,j), at a given frequency and time (k, l);
• and,
• for each spatial segment (i,j)
• providing a first database (Ψij) of training signals comprising a multitude of pairs of corresponding noisy beamformed signals X(k, l, θi, rj) representative of sound in the spatial segment in question and associated values of said speech presence indicator function I(k, l, θi, rj) in a time frequency representation, wherein said values of said speech presence indicator function represent ground truth values;
• and
• determining optimized parameters (Ψ*ij) of an algorithm for estimating said speech presence probability by optimizing it with at least some of said noisy beamformed signals X(k, l, θi, rj) and said associated values of said speech presence indicator function I(k, l, θi, rj) of said first database (Ψij) the algorithm providing corresponding estimated speech presence indicator values Î(k, l, θi, rj), said optimization of parameters (Ψ*ij) being conducted under a constraint of minimizing a cost function of said estimated speech presence indicator values.

A method of operating a hearing device, the method comprising:

• providing a multitude of electric input signals representing sound in the environment of the user in a time-frequency representation, wherein the sound is a mixture of speech and additive noise or other distortions;
• providing a multitude of beamformed signals, each being representative of the sound in a different one of a multitude of spatial segments around the user, and each being based on at least two of said multitude of electric input signals;
• providing for each of said multitude of spatial segments an estimate of a probability Pij(k,l) that the spatial segment in question contains speech at a given point in time and frequency in dependence of the beamformed signals;
• and wherein
• at least one of the multitude of estimates of speech probability is/are provided by a trained neural network.