A method for enhancing telephone speech signals based on convolutional neural networks, the method comprising: • a pre-processing stage which comprises extracting the magnitude and the phase of a spectral representation of the telephone speech signal; • a noise reduction stage which comprises applying the following steps to the magnitude of the spectral representation of the telephone speech signal: ∘ applying a spectral estimator; ∘ computing a perceptual representation (16,17); ∘ applying a Convolutional Neural Network which, with inputs corresponding to the spectral estimate and the perceptual representation (16,17), generates as an output a Wiener gain estimate consisting of a matrix/vector dependent on the frequency and which varies in time; ∘ using the Wiener gain estimate within the enhancement filter of the function f1: G filtro t f = G ^ Wiener t f exp 1 2 ∫ ν t f ∞ e − t t dt p t f G min 1 − p t f wherein t is the time segment, f is the frequency bin, Ĝ Wiener = DNN ( x t, x t -1,...) with x t the vector of spectral and perceptual parameters, G min is a constant p ( t, f) is the speech presence probability and v t f = G ^ Wiener 1 − G ^ Wiener; and also using the Wiener gain estimate as a probability of the presence of speech; ∘ applying the previous function f1 as a speech enhancement filter; • a post-processing stage which comprises merging the initial phase with the magnitude enhanced in the noise reduction stage. • a pre-processing stage which comprises extracting the magnitude and the phase of a spectral representation of the telephone speech signal; • a noise reduction stage which comprises applying the following steps to the magnitude of the spectral representation of the telephone speech signal: ∘ applying a spectral estimator; ∘ computing a perceptual representation (16,17); ∘ applying a Convolutional Neural Network which, with inputs corresponding to the spectral estimate and the perceptual representation (16,17), generates as an output a Wiener gain estimate consisting of a matrix/vector dependent on the frequency and which varies in time; ∘ using the Wiener gain estimate within the enhancement filter of the function f1: G filtro t f = G ^ Wiener t f exp 1 2 ∫ ν t f ∞ e − t t dt p t f G min 1 − p t f wherein t is the time segment, f is the frequency bin, Ĝ Wiener = DNN ( x t, x t -1,...) with x t the vector of spectral and perceptual parameters, G min is a constant p ( t, f) is the speech presence probability and v t f = G ^ Wiener 1 − G ^ Wiener; and also using the Wiener gain estimate as a probability of the presence of speech; ∘ applying the previous function f1 as a speech enhancement filter; ∘ applying a spectral estimator; ∘ computing a perceptual representation (16,17); ∘ applying a Convolutional Neural Network which, with inputs corresponding to the spectral estimate and the perceptual representation (16,17), generates as an output a Wiener gain estimate consisting of a matrix/vector dependent on the frequency and which varies in time; ∘ using the Wiener gain estimate within the enhancement filter of the function f1: G filtro t f = G ^ Wiener t f exp 1 2 ∫ ν t f ∞ e − t t dt p t f G min 1 − p t f wherein t is the time segment, f is the frequency bin, Ĝ Wiener = DNN ( x t, x t -1,...) with x t the vector of spectral and perceptual parameters, G min is a constant p ( t, f) is the speech presence probability and v t f = G ^ Wiener 1 − G ^ Wiener; and also using the Wiener gain estimate as a probability of the presence of speech; ∘ applying the previous function f1 as a speech enhancement filter; • a post-processing stage which comprises merging the initial phase with the magnitude enhanced in the noise reduction stage.