SPEAKER RECOGNITION USING ADAPTIVE THRESHOLDING

• US 20170287490A1
• Filed: 03/29/2016
• Published: 10/05/2017
• Est. Priority Date: 03/29/2016
• Status: Active Grant

• Current Assignee: Hyundai Motor Company (Hyundai Motor Group), Kia Corp.
• Original Assignee: Intel Corporation
• Inventors: Biswal, Narayan, Cilingir, Gokcen
• Granted Patent: US 9,972,322 B2
• Time in Patent Office: Days
• CPC Class Codes: G10L 15/20 Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech G10L21/02 takes precedence
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 17/02 Preprocessing operations, e.g. segment selection; Pattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal components; Feature selection or extraction
• CPC Class Codes: G10L 17/04 Training, enrolment or model building
• CPC Class Codes: G10L 17/06 Decision making techniques; Pattern matching strategies
• CPC Class Codes: G10L 17/20 Pattern transformations or operations aimed at increasing system robustness, e.g. against channel noise or different working conditions
• CPC Class Codes: G10L 17/22 Interactive procedures; Man-machine interfaces
• 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: G10L 25/81 for discriminating voice from music

Abstract

Techniques related to speaker recognition are discussed. Such techniques may include determining an adaptive speaker recognition threshold based on a speech to noise ratio and noise type label corresponding to received audio and performing speaker recognition based on the adaptive speaker recognition threshold and a speaker recognition score corresponding to received audio.

Claims

1. A computer-implemented method for speaker recognition comprising:

determining a speaker recognition score based on a received audio input;

generating a speech to noise ratio based on the received audio input;

generating a noise type label corresponding to the received audio input;

determining an adaptive speaker recognition threshold based at least in part on the speech to noise ratio and the noise type label; and

performing speaker recognition for the received audio input based on a comparison of the speaker recognition score to the speaker acceptance threshold.

2. The method of claim 1, wherein determining the adaptive speaker recognition threshold comprises determining the speaker acceptance threshold based on the speech to noise ratio, the noise type label, and at least one of a target false accept rate or a target false reject rate corresponding to the received audio input.

3. The method of claim 2, further comprising:

determining the target false accept rate or the target false reject rate based on a speaker recognition application corresponding to the received audio input.

4. The method of claim 3, wherein the target false accept rate comprises a first value when the speaker recognition application comprises a biometric security application and a second value when the speaker recognition application comprises an authenticated command application, and wherein the first value is less than the second value.

5. The method of claim 2, wherein determining the adaptive speaker recognition threshold comprises accessing a look up table based on the speech to noise ratio, the noise type label, and at least one of a target false accept rate or a target false reject rate to determine the speaker acceptance threshold.

6. The method of claim 1, further comprising:

determining a second adaptive speaker recognition threshold based at least in part on a second speech to noise ratio and a second noise type label corresponding to a second received audio input; and

performing a second speaker recognition for the second received audio input based on a second comparison of a second speaker recognition score to the second adaptive speaker recognition threshold, wherein the first and second adaptive speaker recognition thresholds are different.

7. The method of claim 1, wherein performing speaker recognition comprises accepting the received audio input as corresponding to a target user when the speaker recognition score exceeds the adaptive speaker recognition threshold or rejecting the received audio input as corresponding to the target user when the speaker recognition score does not exceed the adaptive speaker recognition threshold.

8. The method of claim 1, further comprising:

determining a second speaker recognition score based on the received audio input, wherein determining the speaker recognition score comprises applying a speaker model corresponding to a first user and determining the second speaker recognition score comprises applying a second speaker model corresponding to a second user, wherein the first and second speaker models are different, and

wherein performing the speaker recognition comprises a second comparison of the second speaker recognition score to the adaptive speaker recognition threshold.

9. The method of claim 8, further comprising:

providing an identified speaker indicator corresponding to the first user or the second user based on the comparison and the second comparison.

10. The method of claim 1, wherein at least one of determining the speaker recognition score, generating the speech to noise ratio, or generating the noise type label comprises extracting features from the received audio input and applying a pretrained classification model to the features.

11. A system for performing speaker recognition comprising:

a memory configured to store a received audio input; and

a digital signal processor coupled to the memory, the digital signal processor to determine a speaker recognition score based on a received audio input, to generate a speech to noise ratio based on the received audio input, to generate a noise type label corresponding to the received audio input, to determine an adaptive speaker recognition threshold based at least in part on the speech to noise ratio and the noise type label, and to perform speaker recognition for the received audio input based on a comparison of the speaker recognition score to the speaker acceptance threshold.

12. The system of claim 11, wherein the digital signal processor to determine the adaptive speaker recognition threshold comprises the digital signal processor to determine the speaker acceptance threshold based on the speech to noise ratio, the noise type label, and at least one of a target false accept rate or a target false reject rate corresponding to the received audio input.

13. The system of claim 12, wherein the digital signal processor is to determine the target false accept rate or the target false reject rate based on a speaker recognition application corresponding to the received audio input.

14. The system of claim 12, wherein the digital signal processor to determine the adaptive speaker recognition threshold comprises the digital signal processor to access a look up table based on the speech to noise ratio, the noise type label, and at least one of a target false accept rate or a target false reject rate to determine the speaker acceptance threshold.

15. The system of claim 11, wherein the digital signal processor is to determine a second adaptive speaker recognition threshold based at least in part on a second speech to noise ratio and a second noise type label corresponding to a second received audio input and to perform a second speaker recognition for the second received audio input based on a second comparison of a second speaker recognition score to the second adaptive speaker recognition threshold, wherein the first and second adaptive speaker recognition thresholds are different.

16. The system of claim 11, wherein the digital signal processor to perform speaker recognition comprises the digital signal processor to accept the received audio input as corresponding to a target user when the speaker recognition score exceeds the adaptive speaker recognition threshold or to reject the received audio input as corresponding to the target user when the speaker recognition score does not exceed the adaptive speaker recognition threshold.

17. The system of claim 11, wherein the digital signal processor is to determine a second speaker recognition score based on the received audio input, wherein to determine the speaker recognition score comprises the digital signal processor to apply a speaker model corresponding to a first user and to determine the second speaker recognition score comprises the digital signal processor to apply a second speaker model corresponding to a second user, wherein the first and second speaker models are different, and

wherein the digital signal processor to perform the speaker recognition comprises the digital signal processor to perform a second comparison of the second speaker recognition score to the adaptive speaker recognition threshold.

18. At least one machine readable medium comprising a plurality of instructions that, in response to being executed on a device, cause the device to provide speaker recognition by:

determining a speaker recognition score based on a received audio input;

generating a speech to noise ratio based on the received audio input;

generating a noise type label corresponding to the received audio input;

determining an adaptive speaker recognition threshold based at least in part on the speech to noise ratio and the noise type label; and

performing speaker recognition for the received audio input based on a comparison of the speaker recognition score to the speaker acceptance threshold.

19. The machine readable medium of claim 18, wherein determining the adaptive speaker recognition threshold comprises determining the speaker acceptance threshold based on the speech to noise ratio, the noise type label, and at least one of a target false accept rate or a target false reject rate corresponding to the received audio input.

20. The machine readable medium of claim 19 further comprising a plurality of instructions that, in response to being executed on the device, cause the device to provide speaker recognition by:

determining the target false accept rate or the target false reject rate based on a speaker recognition application corresponding to the received audio input.

21. The machine readable medium of claim 19, wherein determining the adaptive speaker recognition threshold comprises accessing a look up table based on the speech to noise ratio, the noise type label, and at least one of a target false accept rate or a target false reject rate to determine the speaker acceptance threshold.

22. The machine readable medium of claim 18 further comprising a plurality of instructions that, in response to being executed on the device, cause the device to provide speaker recognition by:

determining a second adaptive speaker recognition threshold based at least in part on a second speech to noise ratio and a second noise type label corresponding to a second received audio input; and

performing a second speaker recognition for the second received audio input based on a second comparison of a second speaker recognition score to the second adaptive speaker recognition threshold, wherein the first and second adaptive speaker recognition thresholds are different.

23. The machine readable medium of claim 18, wherein performing speaker recognition comprises accepting the received audio input as corresponding to a target user when the speaker recognition score exceeds the adaptive speaker recognition threshold or rejecting the received audio input as corresponding to the target user when the speaker recognition score does not exceed the adaptive speaker recognition threshold.

24. The machine readable medium of claim 18 further comprising a plurality of instructions that, in response to being executed on the device, cause the device to provide speaker recognition by:

determining a second speaker recognition score based on the received audio input, wherein determining the speaker recognition score comprises applying a speaker model corresponding to a first user and determining the second speaker recognition score comprises applying a second speaker model corresponding to a second user, wherein the first and second speaker models are different, and

wherein performing the speaker recognition comprises a second comparison of the second speaker recognition score to the adaptive speaker recognition threshold.