A voice biometric speaker identification method divides received speech into segments (after eg. a trigger phrase), derives scoring information 116 (eg. 0th/1st/2nd order sufficient statistics using a Hidden Markov Model HMM) from features 114 (eg. Mel Frequency Cepstral Coefficients, MFCCs using a Deep Neural Network DNN) extracted from the most recent segment, and combines this 118 with stored scores 122 for previous segments to yield a final identification score 124. The scores may be weighted according to quality 112 (eg. speech quality or amount in each section 120).
Speaker identification