A method for enhancement of speech in noise in a hearing aid, the method comprising: - providing a noisy input signal in a plurality of frequency sub-bands (k); - for each of said frequency sub-bands (k) • providing time-frequency coefficients X ( k, m) corresponding to a sequence [ X ( k, m'-D +1)··· X ( k,m')] of observable noisy signal samples, where k is a frequency index and m, m' are time indices, and D is larger than 1; • enhancing said time-frequency coefficients X ( k, m) thereby providing enhanced time-frequency coefficients S ( k, m); • providing a statistical model of speech; • providing a statistical model (31, 33) of noise; • providing said enhanced time-frequency coefficients S(k,m) using said statistical model of speech and said statistical model of noise, while considering said sequence [ X ( k, m'-D +1)··· X ( k, m')] of observable noisy signal samples, and characterized in that the method further comprises: • said statistical model of speech comprises a dictionary of statistical models representing a first number of sequences of spectral samples across time within the same subband, each sequence representing a characteristic speech element; and • wherein said statistical model of noise comprises a dictionary of statistical models representing a second number of different sequences of spectral samples across time within the same subband, each sequence representing a characteristic noise element. - providing a noisy input signal in a plurality of frequency sub-bands (k); - for each of said frequency sub-bands (k) • providing time-frequency coefficients X ( k, m) corresponding to a sequence [ X ( k, m'-D +1)··· X ( k,m')] of observable noisy signal samples, where k is a frequency index and m, m' are time indices, and D is larger than 1; • enhancing said time-frequency coefficients X ( k, m) thereby providing enhanced time-frequency coefficients S ( k, m); • providing a statistical model of speech; • providing a statistical model (31, 33) of noise; • providing said enhanced time-frequency coefficients S(k,m) using said statistical model of speech and said statistical model of noise, while considering said sequence [ X ( k, m'-D +1)··· X ( k, m')] of observable noisy signal samples, and characterized in that the method further comprises: • said statistical model of speech comprises a dictionary of statistical models representing a first number of sequences of spectral samples across time within the same subband, each sequence representing a characteristic speech element; and • wherein said statistical model of noise comprises a dictionary of statistical models representing a second number of different sequences of spectral samples across time within the same subband, each sequence representing a characteristic noise element. • providing time-frequency coefficients X ( k, m) corresponding to a sequence [ X ( k, m'-D +1)··· X ( k,m')] of observable noisy signal samples, where k is a frequency index and m, m' are time indices, and D is larger than 1; • enhancing said time-frequency coefficients X ( k, m) thereby providing enhanced time-frequency coefficients S ( k, m); • providing a statistical model of speech; • providing a statistical model (31, 33) of noise; • providing said enhanced time-frequency coefficients S(k,m) using said statistical model of speech and said statistical model of noise, while considering said sequence [ X ( k, m'-D +1)··· X ( k, m')] of observable noisy signal samples, and characterized in that the method further comprises: • said statistical model of speech comprises a dictionary of statistical models representing a first number of sequences of spectral samples across time within the same subband, each sequence representing a characteristic speech element; and • wherein said statistical model of noise comprises a dictionary of statistical models representing a second number of different sequences of spectral samples across time within the same subband, each sequence representing a characteristic noise element.