A speech classification apparatus for classification of attended speech in a multi-talker listening environment in hearing devices with electroencephalography, EEG, dependent sound processing, wherein the hearing devices being adapted for being located at or in an ear or for being fully or partially implanted in the head of a user, comprising: a sound processing unit configured to capturing sound input signals from at least one external microphone and segmenting said captured sound input signals into segmented sound signals (17a-e); a speech classification unit comprising: a speech cepstrum calculation unit configured to calculate a speech cepstrum for each segmented sound signal (17a-e), an EEG cepstrum calculation unit configured to calculate an EEG cepstrum for an EEG signal of a user's brain, a mapping unit configured to select a predetermined number of cepstral coefficients from each calculated speech cepstrum and from a calculated EEG cepstrum from an EEG signal of the user's brain, and a correlation unit configured to calculate a correlation value for each segmented sound signal (17a-e) based on a correlation of the selected cepstral coefficients from the calculated speech cepstrum with the selected cepstral coefficient from the calculated EEG cepstrum, and to identify an attended sound signal, which the user is listening to, based on the highest correlation value; wherein an attended speech source is classified based on the identified attended sound signal. a sound processing unit configured to capturing sound input signals from at least one external microphone and segmenting said captured sound input signals into segmented sound signals (17a-e); a speech classification unit comprising: a speech cepstrum calculation unit configured to calculate a speech cepstrum for each segmented sound signal (17a-e), an EEG cepstrum calculation unit configured to calculate an EEG cepstrum for an EEG signal of a user's brain, a mapping unit configured to select a predetermined number of cepstral coefficients from each calculated speech cepstrum and from a calculated EEG cepstrum from an EEG signal of the user's brain, and a correlation unit configured to calculate a correlation value for each segmented sound signal (17a-e) based on a correlation of the selected cepstral coefficients from the calculated speech cepstrum with the selected cepstral coefficient from the calculated EEG cepstrum, and to identify an attended sound signal, which the user is listening to, based on the highest correlation value; a speech cepstrum calculation unit configured to calculate a speech cepstrum for each segmented sound signal (17a-e), an EEG cepstrum calculation unit configured to calculate an EEG cepstrum for an EEG signal of a user's brain, a mapping unit configured to select a predetermined number of cepstral coefficients from each calculated speech cepstrum and from a calculated EEG cepstrum from an EEG signal of the user's brain, and a correlation unit configured to calculate a correlation value for each segmented sound signal (17a-e) based on a correlation of the selected cepstral coefficients from the calculated speech cepstrum with the selected cepstral coefficient from the calculated EEG cepstrum, and to identify an attended sound signal, which the user is listening to, based on the highest correlation value; to calculate a correlation value for each segmented sound signal (17a-e) based on a correlation of the selected cepstral coefficients from the calculated speech cepstrum with the selected cepstral coefficient from the calculated EEG cepstrum, and to identify an attended sound signal, which the user is listening to, based on the highest correlation value; wherein an attended speech source is classified based on the identified attended sound signal.