First and second components representing respective acoustic classes of speech are extracted 72 from a voice audio signal 70 and compared 78 to models 76 of the classes of speech of an enrolled user, and information is obtained about at a channel and/or noise affecting the audio signal. The components may be analysed by comparing magnitudes at frequencies in a spectrum of the audio with the model spectrum. A speaker may be identified 74 provisionally to select the model to use, followed by a compensation 134 for the channel and/or noise, and the compensated audio is used to make a final decision on the speaker’s identity 124. If noise exceeds a threshold in part of a frequency spectrum, that part may be ignored. The acoustic classes of speech may comprise voiced and unvoiced speech, different phoneme classes, or fricatives and sibilants. In another aspect, it is determined whether a channel has a characteristic of a loudspeaker indicating a spoofing replay attack. A loudspeaker characteristic may include a low or high frequency roll-off indicated by the channel decreasing at a constant rate below or above a cut-off frequency, or determining that the channel has a ripple in a pass-band.
Analysing speech signals