MOBILE DEVICE CONTEXT INFORMATION USING SPEECH DETECTION

• US 20130090926A1
• Filed: 06/01/2012
• Published: 04/11/2013
• Est. Priority Date: 09/16/2011
• Status: Abandoned Application

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Grokop, Leonard Henry, Sadasivam, Shankar
• Application Number:
  US13/486,878
• Publication Number:
  US 20130090926A1
• Time in Patent Office: Days
• US Class Current: 704/243
• CPC Class Codes: G10L 15/08 Speech classification or search
• CPC Class Codes: G10L 19/02 using spectral analysis, e.g. transform vocoders or subband vocoders
• CPC Class Codes: G10L 2021/02087 the noise being separate speech, e.g. cocktail party
• CPC Class Codes: G10L 2025/783 based on threshold decision
• CPC Class Codes: G10L 25/51 for comparison or discrimination
• CPC Class Codes: G10L 25/78 Detection of presence or absence of voice signals switching of direction of transmission by voice frequency in two-way loud-speaking telephone systems H04M9/10
• CPC Class Codes: H04M 1/72454 according to context-related or environment-related conditions
• CPC Class Codes: H04M 2250/74 with voice recognition means

Abstract

Systems and methods for speech detection in association with a mobile device are described herein. A method described herein for identifying presence of speech associated with a mobile device includes obtaining a plurality of audio samples from the mobile device while the mobile device operates in a mode distinct from a voice call operating mode, generating spectrogram data from the plurality of audio samples, and determining whether the plurality of audio samples include information indicative of speech by classifying the spectrogram data.

Claims

1. A method for identifying presence of speech associated with a mobile device, the method comprising:

obtaining a plurality of audio samples from the mobile device while the mobile device operates in a mode distinct from a voice call operating mode;

generating spectrogram data from the plurality of audio samples; and

determining whether the plurality of audio samples include information indicative of speech by classifying the spectrogram data.

2. The method of claim 1 wherein the obtaining comprises obtaining noncontiguous samples of ambient audio at an area near the mobile device.

3. The method of claim 1 wherein the determining comprises classifying the spectrogram data using at least one support vector machine (SVM).

4. The method of claim 1 wherein the classifying comprises:

partitioning the spectrogram data into temporal frames;

obtaining individual decisions for each of the frames indicative of whether speech is detected in respective ones of the frames; and

combining the individual decisions to obtain an overall decision relating to whether the plurality of audio samples include information indicative of speech.

5. The method of claim 4 wherein the combining comprises combining the individual decisions based on a number of individual decisions for which speech is detected relative to a total number of the individual decisions.

6. The method of claim 5 wherein the combining further comprises comparing the number of individual decisions for which speech is detected to a threshold that is based on at least one of a desired detection probability or a desired false alarm probability.

7. The method of claim 4 wherein the partitioning comprises partitioning the spectrogram data into non-overlapping temporal frames.

8. The method of claim 4 wherein the obtaining the individual decisions comprises computing a statistical proximity of features of the spectrogram data for each of the frames to features of a reference speech model.

9. The method of claim 8 further comprising generating the reference speech model using a training procedure.

10. The method of claim 1 further comprising randomizing an order of the plurality of audio samples prior to generating the spectrogram data.

11. A speech detection system comprising:

an audio sampling module configured to obtain a plurality of audio samples associated with an area at which a device is located while the device operates in a mode distinct from a voice call operating mode;

an audio spectrogram module communicatively coupled to the audio sampling module and configured to generate spectrogram data from the plurality of audio samples; and

a classifier module communicatively coupled to the audio spectrogram module and configured to determine whether the plurality of audio samples include information indicative of speech by classifying the spectrogram data.

12. The system of claim 11 wherein the audio sampling module is further configured to obtain the plurality of audio samples by obtaining noncontiguous samples of ambient audio associated with the area at which the device is located.

13. The system of claim 11 wherein the classifier module is further configured to classify the spectrogram data using at least one support vector machine (SVM).

14. The system of claim 11 wherein:

the audio spectrogram module is further configured to partition the spectrogram data into temporal frames; and

the classifier module is further configured to classify the spectrogram data by obtaining individual decisions for each of the frames indicative of whether speech is detected in respective ones of the frames and combining the individual decisions to obtain an overall decision relating to whether the plurality of audio samples include information indicative of speech.

15. The system of claim 14 wherein the classifier module is further configured to combine the individual decisions by comparing a number of individual decisions for which speech is detected to a threshold, and wherein the threshold is based on at least one of a desired detection probability or a desired false alarm probability.

16. The system of claim 14 wherein the audio spectrogram module is further configured to partition the spectrogram data into non-overlapping temporal frames.

17. The system of claim 14 wherein the classifier module is further configured to classify the spectrogram data by computing a statistical proximity of features of the spectrogram data for each of the frames to features of a reference speech model.

18. The system of claim 17 wherein the classifier module is further configured to generate the reference speech model using a training procedure.

19. The system of claim 11 wherein the audio sampling module is further configured to randomize an order of the plurality of audio samples prior to processing of the audio samples by the audio spectrogram module.

20. The system of claim 11 further comprising a microphone communicatively coupled to the audio sampling module and configured to produce an audio signal based on ambient audio associated with the area at which the device is located, wherein the audio sampling module is configured to obtain the audio samples from the audio signal.

21. The system of claim 11 wherein the device is a mobile wireless communication device.

22. A system for detecting presence of speech in an area associated with a mobile device, the system comprising:

sampling means for obtaining a plurality of audio samples from the area associated with the mobile device while the mobile device operates in a mode distinct from a voice call operating mode;

spectrogram means, communicatively coupled to the sampling means, for generating a spectrogram comprising spectral density data corresponding to the plurality of audio samples; and

classifier means, communicatively coupled to the spectrogram means, for determining whether the plurality of audio samples include information indicative of speech by classifying the spectral density data of the spectrogram.

23. The system of claim 22 wherein the sampling means comprises means for obtaining noncontiguous samples of ambient audio from the area associated with the mobile device.

24. The system of claim 22 wherein the classifier means comprises means for classifying the spectral density data of the spectrogram using at least one support vector machine (SVM).

25. The system of claim 22 wherein:

the spectrogram means comprises means for partitioning the spectrogram into temporal frames; and

the classifier means comprises means for obtaining individual decisions for each of the frames of the spectrogram indicative of whether speech is detected in respective ones of the frames and means for combining the individual decisions to obtain an overall decision relating to whether the plurality of audio samples include information indicative of speech.

26. The system of claim 25 wherein the classifier means further comprises means for combining the individual decisions by comparing a number of individual decisions for which speech is detected to a threshold, and wherein the threshold is based on at least one of a desired detection probability or a desired false alarm probability.

27. The system of claim 25 wherein the spectrogram means further comprises means for partitioning the spectrogram into non-overlapping temporal frames.

28. The system of claim 25 wherein the classifier means further comprises means for classifying the spectrogram by computing a statistical proximity of features of the spectrogram for each of the frames to features of a reference speech model.

29. The system of claim 28 wherein the classifier means further comprises means for generating the reference speech model using a training procedure.

30. The system of claim 22 wherein the sampling means comprises means for randomizing an order of the plurality of audio samples prior to processing of the audio samples by the spectrogram means.

31. A computer program product residing on a processor-executable computer storage medium, the computer program product comprising processor-executable instructions configured to cause a processor to:

obtain a plurality of audio samples from an area associated with a mobile device while the mobile device operates in a mode distinct from a voice call operating mode;

generate a spectrogram comprising spectral density data corresponding to the plurality of audio samples; and

determine whether the plurality of audio samples include information indicative of speech by classifying the spectral density data of the spectrogram.

32. The computer program product of claim 31 wherein the instructions configured to cause the processor to obtain the plurality of audio samples are further configured to cause the processor to obtain noncontiguous samples of ambient audio from the area associated with the mobile device.

33. The computer program product of claim 31 wherein the instructions configured to cause the processor to determine are further configured to cause the processor to classify the spectral density data of the spectrogram using at least one support vector machine (SVM).

34. The computer program product of claim 31 wherein:

the instructions configured to cause the processor to generate the spectrogram are further configured to cause the processor to partition the spectrogram into temporal frames; and

the instructions configured to cause the processor to determine are further configured to cause the processor to obtain individual decisions for each of the frames of the spectrogram indicative of whether speech is detected in respective ones of the frames and to combine the individual decisions to obtain an overall decision relating to whether the plurality of audio samples include information indicative of speech.

35. The computer program product of claim 34 wherein the instructions configured to cause the processor to determine are further configured to cause the processor to combine the individual decisions by comparing a number of individual decisions for which speech is detected to a threshold, and wherein the threshold is based on at least one of a desired detection probability or a desired false alarm probability.

36. The computer program product of claim 34 wherein the instructions configured to cause the processor to generate the spectrogram are further configured to partition the spectrogram into non-overlapping temporal frames.

37. The computer program product of claim 34 wherein the instructions configured to cause the processor to determine are further configured to cause the processor to classify the spectrogram by computing a statistical proximity of features of the spectrogram for each of the frames to features of a reference speech model.

38. The computer program product of claim 37 wherein the instructions configured to cause the processor to determine are further configured to cause the processor to generate the reference speech model using a training procedure.

39. The computer program product of claim 31 wherein the instructions configured to cause the processor to obtain the plurality of audio samples are further configured to cause the processor to randomize an order of the plurality of audio samples prior to generation of the spectrogram.