Real-time stopping decisions for voice authentication

A computer-implemented method for automatically generating decision thresholds for a speaker recognition system, comprising:

• generating a plurality of scores from a plurality of voice samples comprising imposter voice samples and authentic voice samples, wherein a score is generated for each voice sample of the plurality of voice samples at each checkpoint of a plurality of checkpoints;
• computing a false acceptance rate (FAR) value and a false rejection rate (FRR) value for each score of the plurality of scores;
• generating, from the plurality of scores, a Markov Decision Process (MDP) to represent the speaker recognition system, wherein the MDP comprises a plurality of states, a plurality of actions, and a plurality of transition probabilities, a plurality of rewards, and a discount factor, wherein a state of the plurality of states comprises a select score of the plurality of scores at a select checkpoint of the plurality of checkpoints or a terminal state, wherein a transition probability represents a probability of transitioning to a terminal state based on the FAR values and the FRR values generated from the plurality of scores;
• determining, through reinforcement learning, an optimal policy for each state of the MDP that achieves a maximum cumulative reward;
• and
• generating the decision thresholds for each checkpoint of the plurality of checkpoints from the optimal policy, wherein the decision thresholds comprise an accept threshold and a reject threshold for each checkpoint of the plurality of checkpoints.

A hardware storage device having stored thereon computer executable instructions that are structured to be executable by a processor of a computing device to thereby cause the computing device to perform actions to generate decision thresholds for a speaker recognition system that:

• obtain a plurality of voices samples comprising imposter voice samples and authentic voice samples;
• generate a plurality of scores for the plurality of voices samples, wherein a score of the plurality of scores indicates similarity of a select voice sample of a speaker with a known voice print of the speaker at a particular checkpoint;
• compute a false acceptance rate (FAR) value and a false rejection rate (FRR) value for each score at each checkpoint from the plurality of scores;
• generate, from the plurality of scores, a Markov Decision Process (MDP) to represent the speaker recognition system, wherein the MDP comprises a plurality of states, a plurality of actions, and a plurality of transition probabilities, a plurality of rewards, and a discount factor, wherein a state of the plurality of states comprises a select score of the plurality of scores at a select checkpoint of the plurality of checkpoints or a terminal state that terminates processing, wherein the plurality of rewards and the discount factor are set to promote a decision at an earlier checkpoint, wherein a transition probability represents a probability of transitioning to a terminal state of the plurality of states based on the FAR values and the FRR values generated from the plurality of trials;
• generate, through reinforcement learning, an optimal policy for the MDP that achieves a maximum cumulative reward;
• and
• extrapolate the decision thresholds for each checkpoint of the plurality of checkpoints from the optimal policy, wherein the decision thresholds comprise an accept threshold and a reject threshold for each checkpoint of the plurality of checkpoints.