A method of training an algorithm for optimizing intelligibility of speech components of a sound signal, the method comprising, • providing a first database comprising o a multitude of predefined time segments PDTS i, i=1,..., N PDTS, of first electric input signals representing sound, each time segment comprising ▪ a speech component representing at least one phoneme, or syllable, or word, or ▪ a processed or filtered version of said speech component, and ▪ a noise component, and o corresponding measured speech intelligibilities P i, i=1,..., N PDTS, of each of said predefined time segments PDTS i, said measured speech intelligibilities being measured in advance in a listening test or listening tests; • determining optimized first parameters of a first algorithm, which is a first neural network (SIP-NN), by a first optimizing, e.g. training, with at least some of said predefined time segments PDTS i and said corresponding measured speech intelligibilities P i of said first database, the first algorithm providing corresponding predicted speech intelligibilities P est,i, said optimizing being conducted under a constraint of minimizing a cost function, e.g. a mean squared prediction error e i 2 of said predicted speech intelligibilities; • providing a second database (NSIG) comprising, or otherwise providing access to, a multitude of time segments TS j, j=1,..., N TS, of second electric input signals representing sound, each time segment comprising ▪ a speech component representing at least one phoneme, or syllable, or word, or ▪ a processed or filtered version of said speech component, and ▪ a noise component; • determining optimized second parameters of a second algorithm, which is a second neural network (SE-NN), by a second optimizing, e.g. training, with at least some of said multitude of time segments TS j, where said second algorithm is configured to provide processed versions of said second electric input signals exhibiting respective predicted speech intelligibilities P est,j estimated by said first algorithm using said optimized first parameters, which is the first optimized neural network (SIP-NN*), said second optimizing being conducted under a constraint of maximizing said predicted speech intelligibility P est,j, or a processed, e.g. averaged, version thereof. • providing a first database comprising o a multitude of predefined time segments PDTS i, i=1,..., N PDTS, of first electric input signals representing sound, each time segment comprising ▪ a speech component representing at least one phoneme, or syllable, or word, or ▪ a processed or filtered version of said speech component, and ▪ a noise component, and o corresponding measured speech intelligibilities P i, i=1,..., N PDTS, of each of said predefined time segments PDTS i, said measured speech intelligibilities being measured in advance in a listening test or listening tests; o a multitude of predefined time segments PDTS i, i=1,..., N PDTS, of first electric input signals representing sound, each time segment comprising ▪ a speech component representing at least one phoneme, or syllable, or word, or ▪ a processed or filtered version of said speech component, and ▪ a noise component, and ▪ a speech component representing at least one phoneme, or syllable, or word, or ▪ a processed or filtered version of said speech component, and ▪ a noise component, and o corresponding measured speech intelligibilities P i, i=1,..., N PDTS, of each of said predefined time segments PDTS i, said measured speech intelligibilities being measured in advance in a listening test or listening tests; • determining optimized first parameters of a first algorithm, which is a first neural network (SIP-NN), by a first optimizing, e.g. training, with at least some of said predefined time segments PDTS i and said corresponding measured speech intelligibilities P i of said first database, the first algorithm providing corresponding predicted speech intelligibilities P est,i, said optimizing being conducted under a constraint of minimizing a cost function, e.g. a mean squared prediction error e i 2 of said predicted speech intelligibilities; • providing a second database (NSIG) comprising, or otherwise providing access to, a multitude of time segments TS j, j=1,..., N TS, of second electric input signals representing sound, each time segment comprising ▪ a speech component representing at least one phoneme, or syllable, or word, or ▪ a processed or filtered version of said speech component, and ▪ a noise component; ▪ a speech component representing at least one phoneme, or syllable, or word, or ▪ a processed or filtered version of said speech component, and ▪ a noise component; • determining optimized second parameters of a second algorithm, which is a second neural network (SE-NN), by a second optimizing, e.g. training, with at least some of said multitude of time segments TS j, where said second algorithm is configured to provide processed versions of said second electric input signals exhibiting respective predicted speech intelligibilities P est,j estimated by said first algorithm using said optimized first parameters, which is the first optimized neural network (SIP-NN*), said second optimizing being conducted under a constraint of maximizing said predicted speech intelligibility P est,j, or a processed, e.g. averaged, version thereof.