Methods and systems for splitting a digital signal

1. A method for authenticating users comprising:

calculating signal features for an audio digital signal representing a user utterance, the signal features including onset values, the onset values correspond to stress accents in the signal;

splitting, by a processor, the signal into a prosodic unit candidate sequence based on the stress accent locations;

modifying, by the processor, the prosodic unit candidate sequence based on the signal features, the modified prosodic unit sequence including only true prosodic units

calculating a first probability that the true prosodic units match a prosodic unit sequence used to create a hidden Markov model for the user;

calculating a second probability that the true prosodic units match prosodic unit sequences used to create a Universal Background Model; and

successfully authenticating the user when the difference between the first probability and the second probability is greater than a decision threshold.

2. A method in accordance with claim 1 , said modifying step comprising:

calculating the energy level for each prosodic unit candidate in the prosodic unit candidate sequence; and

removing from the prosodic unit candidate sequence prosodic unit candidates having a low energy level.

3. A method in accordance with claim 1 , said modifying step comprising:

identifying borders, shared by prosodic unit candidates, located in a high level region of the signal; and

moving the identified borders to a low level energy region of the signal.

4. A method in accordance with claim 1 , further comprising:

creating a hidden Markov model in which each true prosodic unit defines a state in the model; and

optimizing the model.

5. A computing system for authenticating users comprising:

a processor; and

a memory configured to store data, said computing device being associated with a network and said memory being in communication with said processor and having instructions stored therein which, when read and executed by said processor cause said computing device to:

calculate signal features for an audio digital signal representing a user utterance, the signal features including onset values, the onset values correspond to stress accents in the signal;

split the signal into a prosodic unit candidate sequence based on the stress accent locations;

modify the prosodic unit candidate sequence based on the signal features, the modified prosodic unit sequence including only true prosodic units;

calculate a first probability that the true prosodic units match a prosodic unit sequence used to create a hidden Markov model for the user;

calculate a second probability that the true prosodic units match prosodic unit sequences used to create a Universal Background Model; and

successfully authenticate the user when the difference between the first probability and the second probability is greater than a decision threshold.

6. A computing system in accordance with claim 5 , wherein the instructions when executed by said processor further cause said computing device to:

calculate the energy level for each prosodic unit candidate in the prosodic unit candidate sequence; and

remove from the prosodic unit candidate sequence prosodic unit candidates having a low energy level.

7. A computing system in accordance with claim 5 , wherein the instructions when executed by said processor further cause said computing device to:

identify borders, shared by prosodic unit candidates, located in a high level region of the signal; and

move the identified borders to a low level energy region of the signal.

8. A computing system in accordance with claim 5 , wherein the instructions when executed by said processor further cause said computing device to:

create a hidden Markov model in which each true prosodic unit defines a state in the model; and

optimize the model.

9. A computing system in accordance with claim 5 , wherein the instructions when executed by said processor further cause said computing device to compute a decision score using the hidden Markov model.

10. A computing system in accordance with claim 5 , said computing device being a smart phone or a tablet computer.

11. A computing system in accordance with claim 5 , said computing device being an authentication computer system.

12. A computer program recorded on a non-transitory computer-readable recording medium included in a computing device for generating trustworthy authentication transaction results, the computer program being comprised of instructions, which when read and executed by the computing device, cause the computing device to:

calculate signal features for an audio digital signal representing a user utterance, the signal features including onset values, the onset values correspond to stress accents in the signal;

split the signal into a prosodic unit candidate sequence based on the stress accent locations;

modify the prosodic unit candidate sequence based on the signal features, the modified prosodic unit sequence including only true prosodic units;

calculate a first probability that the true prosodic units match a prosodic unit sequence used to create a hidden Markov model for the user;

calculate a second probability that the true prosodic units match prosodic unit sequences used to create a Universal Background Model; and

successfully authenticate the user when the difference between the first probability and the second probability is greater than a decision threshold.

13. A computer program in accordance with claim 12 wherein the instructions which when read and executed by the computing device, further cause the computing device to:

calculate the energy level for each prosodic unit candidate in the prosodic unit candidate sequence; and

remove from the prosodic unit candidate sequence prosodic unit candidates having a low energy level.

14. A computer program in accordance with claim 12 wherein the instructions which when read and executed by the computing device, further cause the computing device to:

identify borders, shared by prosodic unit candidates, located in a high level region of the signal; and

move the identified borders to a low level energy region of the signal.

15. A computer program in accordance with claim 12 wherein the instructions which when read and executed by the computing device, further cause the computing device to:

create a hidden Markov model in which each true prosodic unit defines a state in the model; and

optimize the model.

16. A computer program in accordance with claim 12 wherein the instructions which when read and executed by the computing device, further cause the computing device to compute a decision score using the hidden Markov model.