System and method for providing an audio challenge to distinguish a human from a computer

• US 20040254793A1
• Filed: 06/12/2003
• Published: 12/16/2004
• Est. Priority Date: 06/12/2003
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Droppo, James Garnet III, Steinbok, Jeff, Calinov, Iulian, Benaloh, Josh, Herley, Cormac, Goodman, Joshua
• Application Number:
  US10/459,912
• Publication Number:
  US 20040254793A1
• Time in Patent Office: Days
• US Class Current: 704/270
• CPC Class Codes: G10L 13/00 Speech synthesis; Text to speech systems

Abstract

An “audio challenger” operates by first defining a library of a finite number of discrete audio objects including spoken sounds, such as, for example, individual digits, letters, numbers, words, etc., or combinations of two or more digits, letters, numbers, or words. The spoken sounds are either automatically generated by a computer, or recorded from one or more actual spoken voices. Given this library of audio objects, the audio challenger automatically selects one or more audio objects from the library and concatenates the objects into an audio string that is then automatically processed to add one or more distortions to create a “challenge string.” The distorted challenge string is then presented to an unknown party for identification. If the unknown party correctly identifies the challenge string, then the unknown party is deemed to be a human operator. Otherwise, the unknown party is deemed to be another computer.

Claims

1. A computer-implemented process for providing an automatic human interactive proof, comprising:

selecting two or more audio objects from a library comprising a plurality of audio objects;

concatenating the selected audio objects into an audio string;

distorting the audio string with one or more distortions; and

presenting the distorted audio string to an unknown user for identification.

2. The computer-implemented process of claim 1 wherein distorting the audio string comprises generating random babble noise and adding the random babble noise to the audio string.

3. The computer-implemented process of claim 2 wherein generating random babble noise comprises:

randomly sampling one or more segments of speech, with each random sample being equal in duration to the audio string; and

overlaying each random sample to generate the random babble noise.

4. The computer-implemented process of claim 3 further comprising randomly weighting each random sample prior to overlaying the random samples to generate the random babble noise.

5. The computer-implemented process of claim 1 wherein distorting the audio string comprises adding reverberation random babble noise to the audio string.

6. The computer-implemented process of claim 1 wherein distorting the audio string comprises adding reverberation to the audio string.

7. The computer-implemented process of claim 1 wherein distorting the audio string comprises adding popping noise to the audio string.

8. The computer-implemented process of claim 1 wherein distorting the audio string comprises clipping the audio string.

9. The computer-implemented process of claim 1 wherein distorting the audio string comprises adding narrow band sounds to the audio string.

10. The computer-implemented process of claim 1 wherein distorting the audio string comprises applying time domain distortions to the audio string.

11. The computer-implemented process of claim 1 wherein distorting the audio string comprises applying frequency domain distortions to the audio string.

12. The computer-implemented process of claim 1 wherein distorting includes distorting the audio string with two or more of:

adding random babble noise to the audio string;

adding reverberation to the audio string;

adding popping noise to the audio string;

clipping the audio string;

adding narrow band sounds to the audio string;

applying time domain distortions to the audio string; and

applying frequency domain distortions to the audio string.

13. The computer-implemented process of claim 1 wherein each of the plurality of audio objects in the library includes a speech clip representing any of digits, letters, numbers, and words, and a character-based representation of each speech clip.

14. The computer-implemented process of claim 1 wherein concatenating the selected audio objects into an audio string includes inserting random pauses between each audio object.

15. The computer-implemented process of claim 1 wherein parameters of the one or more distortions are varied with each instance of producing the distorted audio string.

16. The computer-implemented process of claim 1 further comprising comparing a textual response to distorted audio string.

17. The computer-implemented process of claim 16 wherein the unknown user is identified as human if the textual response matches the audio objects represented by the distorted audio string.

18. The computer-implemented process of claim 16 wherein the unknown user is identified as human if the textual response at least partially matches the audio objects represented by the distorted audio string within a predetermined error threshold.

19. The computer-implemented process of claim 16 wherein the unknown user is identified as a computing device if the textual response does not match the audio objects represented by the distorted audio string.

20. The computer-implemented process of claim 16 wherein the unknown user is identified as a computing device if the textual response does not at least partially match the audio objects represented by the distorted audio string within a predetermined error threshold.

21. The computer-implemented process of claim 1 further comprising user selection of an audio object language.

22. A system for determining whether an unknown computer user is a human, comprising:

automatically selecting two or more audio objects from an object library;

automatically concatenating the selected audio objects into an audio string;

generating babble noise by sampling one or more segments of speech and overlaying each sample to generate the babble noise;

adding the babble noise to the audio string to create an audio challenge string;

presenting the audio challenge string to an unknown computer user for identification;

comparing a textual response from the unknown user to the prerecorded audio objects selected from the object library; and

determining the unknown user to be human if the textual response matches the audio objects selected from the object library within a predetermined error threshold.

23. The system of claim 22 further comprising user selection of an object library language for use in creating the audio challenge string.

24. The system of claim 22 further comprising randomly weighting each sample prior to overlaying the samples to generate the babble noise.

25. The system of claim 22 wherein the segments of speech are any of segments of human speech and automatically synthesized segments of speech.

26. The system of claim 22 further comprising distorting the audio string by at least two of:

adding reverberation to the audio string;

adding popping noise to the audio string;

clipping the audio string;

adding narrow band sounds to the audio string;

applying time domain distortions to the audio string; and

applying frequency domain distortions to the audio string.

27. The system of claim 26 wherein parameters of at least one distortion of the audio string are randomized with each instance of creating the audio challenge string.

28. The system of claim 22 wherein each of the audio objects in the object library includes a speech clip representing any of digits, letters, numbers, and words, and a character-based representation of each speech clip.

29. The system of claim 22 wherein concatenating the selected audio objects further comprises inserting random temporal spaces between each audio object.

30. The system of claim 29 wherein the random temporal spaces are filled with noise.

31. The system of claim 22 further comprising determining the unknown user not to be human if the textual response does not match the audio objects selected from the object library within a predetermined error threshold.

32. A method for generating an audio-based challenge for an automated human interactive proof, comprising:

automatically selecting two or more audio objects;

automatically concatenating the selected audio objects into an audio string with the addition of random temporal spaces between each audio object;

applying a randomized reverberation filter to the concatenated audio string; and

presenting the filtered audio string as an audio challenge to an unknown computer user for identification.

33. The method of claim 32 further comprising a user selectable language to be used in generating the audio challenge.

34. The method of claim 32 further comprising automatically comparing a textual response from the unknown computer user to the selected audio objects and identifying the unknown user as human when the textual response matches the selected audio objects.

35. The method of claim 32 further comprising automatically comparing a textual response from the unknown computer user to the selected audio objects and identifying the unknown user as a computer when the textual response does not match the selected audio objects.

36. The method of claim 32 further comprising generating random babble noise by randomly sampling one or more segments of speech and overlaying each sample to generate the random babble noise and adding the random babble noise to the filtered audio string before presenting the filtered audio string as an audio challenge to the unknown computer user.

37. The method of claim 36 further comprising randomly weighting each random sample prior to overlaying the random samples.

38. The method of claim 36 wherein the segments of speech are any of segments of human speech and segments of automatically synthesized speech.

39. The method of claim 32 further comprising distorting the filtered audio string before presenting the filtered audio string as an audio challenge to the unknown computer user by at least one of:

adding popping noise to the audio string;

clipping the audio string;

adding narrow band sounds to the audio string;

applying time domain distortions to the audio string; and

applying frequency domain distortions to the audio string.

40. The method of claim 32 wherein parameters defining of one or more of the distortions of the filtered audio string are randomly varied with each instance of generating the audio challenge.

41. The method of claim 32 wherein the random temporal spaces are filled with white noise.