Identifying audio content using distorted target patterns

• US 8,615,397 B2
• Filed: 04/04/2008
• Issued: 12/24/2013
• Est. Priority Date: 04/04/2008
• Status: Active Grant

• Current Assignee: Intuit, Inc.
• Original Assignee: Intuit, Inc.
• Inventors: Hart, Matt E.
• Primary Examiner(s): ALBERTALLI, BRIAN LOUIS
• Application Number:
  US12/098,016
• Publication Number:
  US 20090254343A1
• Time in Patent Office: 2,090 Days
• Field of Search: None
• US Class Current: 704/243
• CPC Class Codes: G10L 15/065 Adaptation

Abstract

Embodiments of a system for identifying audio content are described. During operation, the system receives a data stream from an electronic device via a communication network. Then, the system distorts a set of target patterns which are used to identify the audio content based on characteristics of the electronic device and/or the communication network. Next, the system identifies the audio content in the data stream based on the set of distorted target patterns.

Claims

1. A method for identifying audio content, comprising:

receiving a data stream from an electronic device via a communication network, wherein the data stream includes training data and audio content that is to be identified, and wherein the training data and the audio content in the data stream are distorted by dynamic characteristics of the electronic device and the communication network;

determining, from the received distorted training data in the data stream, dynamic characteristics of the electronic device and the communication network;

dynamically distorting, using a computer, a set of target patterns for identifying the distorted audio content based on the determined dynamic characteristics of the electronic device and the communication network, wherein dynamically distorting the set of target patterns comprises using an encoding technique to perform the distortion; and

identifying the distorted audio content in the data stream based on the set of distorted target patterns, wherein the distorted audio content spectrum matches with the set of distorted target patterns.

2. The method of claim 1, wherein the electronic device includes a cellular telephone;

and

wherein the communication network includes a cellular-telephone network.

3. The method of claim 1, wherein communication via the communication network involves wireless communication.

4. The method of claim 1, wherein the audio content includes spoken information.

5. The method of claim 1, wherein the set of target patterns corresponds to a dictionary of words.

6. The method of claim 1, wherein distorting the set of target patterns includes changing an amplitude at a given frequency in a range of audio frequencies associated with the set of target patterns.

7. The method of claim 1, wherein the set of target patterns is distorted using a filter.

8. The method of claim 1, wherein the set of target patterns is distorted based on characteristics associated with a user of the electronic device.

9. The method of claim 1, wherein the training data includes audio data for predetermined frequencies.

10. The method of claim 1, wherein the training data is associated with a predetermined phrase.

11. The method of claim 1, wherein the training data is associated with a predetermined key-pad pattern on the electronic device.

12. The method of claim 1, wherein the training data is provided by the electronic device.

13. The method of claim 1, wherein the training data comprises audio data for a predetermined key-pad pattern on the electronic device that the user inputs.

14. The method of claim 1, further comprising:

transcribing the audio content into text; and

associating the text with a financial transaction for the user based on an identifier for the electronic device.

15. The method of claim 14, wherein financial software performs the associating the text with the financial transaction.

16. The method of claim 15, wherein the financial software includes payroll software, income-tax software, accounting software, or customer-relationship-management software.

17. The method of claim 14, wherein associating the text with a financial transaction further comprises associating the text with the financial transaction based on a location for the user.

18. The method of claim 1, wherein distorting the set of target patterns comprises adjusting a distortion for the set of target patterns based on a confidence metric associated with a process of identifying the audio content.

19. The method of claim 1, wherein the encoding technique comprises lossy compression.

20. A computer-program product for use in conjunction with a computer system, the computer-program product comprising a non-transitory computer-readable storage medium and a computer-program mechanism embedded therein for configuring the computer system, the computer-program mechanism including:

instructions for receiving a data stream from an electronic device via a communication network, wherein the data stream includes training data and audio content that is to be identified, and wherein the training data and the audio content in the data stream are distorted by dynamic characteristics of the electronic device and the communication network;

instructions for determining, from the received distorted training data in the data stream, dynamic characteristics of the electronic device and the communication network;

instructions for dynamically distorting a set of target patterns for identifying the audio content based on the determined dynamic characteristics of the electronic device and the communication network, wherein dynamically distorting the set of target patterns comprises using an encoding technique to perform the distortion; and

instructions for identifying distorted audio content in the data stream based on the set of distorted target patterns, wherein the distorted audio content spectrum matches with the set of distorted target patterns.

21. The computer-program product of claim 20, wherein the electronic device includes a cellular telephone;

and

wherein the communication network includes a cellular-telephone network.

22. The computer-program product of claim 20, wherein the audio content includes spoken information.

23. The computer-program product of claim 20, wherein the set of target patterns corresponds to a dictionary of words.

24. The computer-program product of claim 20, wherein the set of target patterns is distorted based on characteristics associated with a user of the electronic device.

25. The computer-program product of claim 20, wherein distorting the set of target patterns comprises adjusting a distortion for the set of target patterns based on a confidence metric associated with a process of identifying the audio content.

26. A computer system, comprising:

a processor;

memory;

a program module, wherein the program module is stored in the memory and configured to be executed by the processor, the program module including;

instructions for receiving a data stream from an electronic device via a communication network, wherein the data stream includes training data and audio content that is to be identified, and wherein the training data and the audio content in the data stream are distorted by dynamic characteristics of the electronic device and the communication network;

instructions for determining, from the received distorted training data in the data stream, dynamic characteristics of the electronic device and the communication network;

instructions for dynamically distorting a set of target patterns for identifying the audio content based on the determined dynamic characteristics of the electronic device and the communication network, wherein distorting the set of target patterns comprises using an encoding technique to perform the distortion;

instructions for identifying distorted audio content in the data stream based on the set of distorted target patterns, wherein the distorted audio content spectrum matches with the set of distorted target patterns.