Speaker Recognition

• US 20160225374A1
• Filed: 09/20/2013
• Published: 08/04/2016
• Est. Priority Date: 09/28/2012
• Status: Active Grant

• Current Assignee: Cirrus Logic Incorporated
• Original Assignee: Agnito SL
• Inventors: Rodriguez, Luis Buera
• Granted Patent: US 9,626,971 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G10L 17/04 Training, enrolment or model building
• CPC Class Codes: G10L 17/10 Multimodal systems, i.e. based on the integration of multiple recognition engines or fusion of expert systems
• CPC Class Codes: G10L 17/14 Use of phonemic categorisation or speech recognition prior to speaker recognition or verification
• CPC Class Codes: G10L 17/16 Hidden Markov models [HMM]
• CPC Class Codes: G10L 17/24 the user being prompted to utter a password or a predefined phrase
• CPC Class Codes: G10L 2015/025 Phonemes, fenemes or fenones being the recognition units

Abstract

Method for text-dependent Speaker Recognition using a speaker adapted Universal Background Model, wherein the speaker adapted Universal Background Model is a speaker adapted Hidden Markov Model comprising channel correction.

Claims

1. A method for text-dependent Speaker Recognition using a speaker model obtained by adaptation of a Universal Background Model;

wherein the speaker model is a speaker adapted Hidden Markov Model.

2. The method for text-dependent Speaker Recognition according to claim 1, wherein the Universal Background Model is unsupervised adapted based on enrolment utterances of the speaker.

3. The method for text-dependent Speaker Recognition according to claim 1, wherein only mean vectors and transition probabilities are adapted in the speaker model or wherein all parameters are adapted in the speaker model, wherein the adaption of the Universal Background Model may be done compensating the channel.

4. The method for text-dependent Speaker Recognition according to claim 1, wherein the Universal Background Model of the text-dependent system is trained in an unsupervised training before it is adapted.

5. The method for text-dependent Speaker Recognition according to claim 1, wherein utterances of a plurality speakers, which speak more than 5 different languages are used for an unsupervised training of the Universal Background Model of the text dependent system.

6. The method for text-dependent Speaker Recognition according to claim 1, wherein the topology of the Universal Background Model of the text-dependent system is selected to comprise a transition possibility from each possible state to itself and each possible other state;

and/or

wherein the observation probability distributions are Gaussian Mixture Models optionally with diagonal covariance matrices and four components;

and/or

wherein the number of states is fixed.

7. The method for text-dependent Speaker Recognition according to claim 1, wherein the number of states is set to less than 30, 30, more than 30, the number of Hungarian phonemes or a number estimated by an analysis of the spectral properties of a signal.

8. The method for text-dependent Speaker Recognition according to claim 1, further comprising adapting one or more parameters, to a lexical content.

9. The method for text-dependent Speaker Recognition according to claim 1, wherein the eigenvoices matrix and eigenchannel matrix are trained from the generic Universal Background Model in a development session.

10. The method for text-dependent Speaker Recognition according to claim 1, further comprising the step of verifying in an unsupervised way whether a test signal was spoken by a target person.

11. The method for text-dependent Speaker Recognition according to claim 1, wherein the speaker adapted model is used only to determine the most likely path, but not to compute the statistics, which may be used to extract the log likelihood ratios, wherein the channel may be compensated.

12. The method for text-dependent Speaker Recognition according to claim 1, wherein verifying whether the test signal was spoken by the targeted person comprises

calculating the difference between

the two terms of the log likelihood of the testing audio and the speaker model and the log product of the transition probabilities of the most likely path obtained with the speaker model

and

the log likelihood of the testing audio and the generic Universal Background Model and the log product of the transition probabilities of the most likely path obtained with the generic Universal Background Model.

13. The method for text-dependent Speaker Recognition according to claim 1, wherein the method further comprises identifying a target person by identifying the speaker adapted model with the highest likelihood score.

14. The method for text-dependent Speaker Recognition according to claim 1, wherein the Universal Background Model is a Hidden Markov Model.

15. The method for text-dependent Speaker Recognition according to claim 1, wherein the mean vectors and the transition probabilities of the Universal Background Model are adapted for the speaker model using a Maximum A Posteriori adaptation.

16. The method for text-dependent Speaker Recognition according to claim 1, wherein the channel factors are compensated in the speaker adapted model.

17. The method for text-dependent Speaker Recognition according to claim 1, wherein the following variables are used in the complete model:

a sequence of speaker factors Y

a sequence of channel factors U

a sequence of the feature vectors X

a sequence of Hidden Markov Model states S

a sequence of Gaussian components Z.

18. The method for text-dependent Speaker Recognition according to claim 1, wherein the dependencies of the variables are described by a Bayesian network.

19. The method for text-dependent Speaker Recognition according to claim 1, wherein in the Bayesian network the observed parameters are the feature vectors x_nmt of utterance m of speaker n and time index t.

21. The method for text-dependent Speaker Recognition according to claim 1, wherein an iterative Expectation Maximization algorithm is applied for the training of the Universal Background Model given the development data, wherein optionally 1, 2, 3, 4, 5 or more iterations are used.

22. The method for text-dependent Speaker Recognition according to claim 21, wherein in the iterative algorithm in some of the iterations an additional step is introduced for maintaining boundary conditions or a step is replaced by a step for maintaining boundary conditions, e.g. a step of divergence re-estimation.

23. The method for text-dependent Speaker Recognition according to claim 1, wherein a speaker dependent Hidden Markov Model is created by adapting the mean vectors and the eigenvoice matrix of the Universal Background Model according to the enrollment data.

24. The method for text-dependent Speaker Recognition according to claim 1, wherein for the training of the Universal Background Model the model is initialized with values found by training a full covariance Universal Background Model, wherein optionally then

the eigenvoice matrix is initialized and the eigenchannel matrix is assumed as 0,

and then optionally a suitable algorithm is applied neglecting any channel factors,

wherein the values thus found are used to initialize the eigenchannel matrix.

25. The method for text-dependent Speaker Recognition according to claim 1, wherein the method is used for speaker verification and/or speaker identification.

27. The method for text-dependent Speaker Recognition according to claim 26, wherein text-dependent speaker recognition according to claim 1 is used.

31. A non-transitory computer readable medium comprising computer readable instructions for executing a method according to claim 1.

32. A system comprising means for executing a method according to claim 1.

33. The method of claim 1, further comprising channel correction.

34. The method of claim 2, wherein statistics are extracted using enrolment feature vectors, the Universal Background Model and a suitable algorithm.

35. The method of claim 34, wherein the suitable algorithm includes one of a forward-backward algorithm or a Viterbi algorithm.

36. The method of claim 8, wherein the one or more parameters includes at least one of the number of states or the Universal Background Model of the text dependent system.

37. The method of claim 8, wherein the lexical content is a passphrase.

38. The method of claim 19, wherein the hidden parameters are at least one of a group of:

the speaker factor y_n for each speaker n,

the channel factors u_nm of the utterance m of speaker n,

the active state s_nmt generating the feature vector x_nmt, and

the active component z_nmt generating the feature vector x_nmt.

40. The method of claim 38, wherein x_nmt depends on y_n, u_nm, s_nmt, and z_nmt, z_nmt depends on s_nmt and s_nmt depends on s_nmt−

1.

20. (canceled)

26. A method for text-dependent Speaker Recognition using a text-dependent and a text-independent system, wherein a model for the text-dependent system, e.g. a Hidden Markov Model, is adapted in an unsupervised way, e.g. by Maximum A Posteriori or Bayesian algorithm, and wherein in addition a model for the text-independent system for the speaker and the phrase is built.

28. The method for text-dependent Speaker Recognition according to claim 26, further comprising the step of verifying in an unsupervised way whether a test signal was spoken by the target person.

29. The method for text-dependent Speaker Recognition according to claim 26, wherein the method further comprises a step of identifying a target person by identifying the speaker adapted model with the highest likelihood score.

30. The method for text-dependent Speaker Recognition according to claim 26, wherein the scalar weights for the linear combination have been trained in a development session.

39. The method of claim 28, wherein verifying whether the test signal was spoken by the target person comprises comparing a linear combination of the log likelihood ratios of the text-dependent system and the log likelihood ratio of the text-independent system with a threshold.