Automatic Text-Independent, Language-Independent Speaker Voice-Print Creation and Speaker Recognition

• US 20080312926A1
• Filed: 05/24/2005
• Published: 12/18/2008
• Est. Priority Date: 05/24/2005
• Status: Abandoned Application

• Current Assignee: Loquendo SpA (Microsoft Corporation)
• Original Assignee: Loquendo SpA (Microsoft Corporation)
• Inventors: Colibro, Daniele, Vair, Claudio, Fissore, Luciano
• Application Number:
  US11/920,849
• Publication Number:
  US 20080312926A1
• Time in Patent Office: Days
• US Class Current: 704/249
• CPC Class Codes: G10L 17/04 Training, enrolment or model building
• 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]

Abstract

An automatic dual-step, text independent, language-independent speaker voice-print creation and speaker recognition method, wherein a neural network-based technique is used in a first step and a Markov model-based technique is used in a second step. In particular, the first step uses a neural network-based technique for decoding the content of what is uttered by the speaker in terms of language independent acoustic-phonetic classes, wherein the second step uses the sequence of language-independent acoustic-phonetic classes from the first step and employs a Markov model-based technique for creating the speaker voice-print and for recognizing the speaker. The combination of the two steps enables improvement in the accuracy and efficiency of the speaker voice-print creation and of the speaker recognition, without setting any constraints on the lexical content of the speaker utterance and on the language thereof.

Claims

1-26. -26. (canceled)

27. A method for creating a voice-print of a speaker based on an input voice signal representing an utterance of said speaker, comprising:

processing said input voice signal to provide a sequence of language-independent acoustic-phonetic classes associated with corresponding temporal segments of said input voice signal, said language-independent acoustic-phonetic classes representing sounds in said utterance and being represented by respective original acoustic models;

adapting the original acoustic model of each of said language-independent acoustic-phonetic classes to the speaker, based on the temporal segment of the input voice signal associated with a language-independent acoustic-phonetic class; and

creating said voice-print based on the adapted acoustic models of said language-independent acoustic-phonetic classes.

28. The method of claim 27, wherein processing said input voice signal comprises:

carrying out a neural network-based decoding.

29. The method of claim 28, wherein said neural network-based decoding is performed by using a hybrid hidden Markov models/artificial neural networks decoder.

30. The method of claim 27, wherein said original acoustic models of said language-independent acoustic-phonetic classes are hidden Markov models.

31. The method of claim 27, wherein processing said input voice signal comprises:

extracting observation vectors from said input voice signal, each observation vector being formed by parameters extracted from the input voice signal at a fixed time frame; and

temporally aligning said observation vectors with said input voice signal so as to associate sets of observation vectors with corresponding temporal segments of the input voice signal; and

wherein adapting the original acoustic model of each of said language-independent acoustic-phonetic classes to the speaker, based on the temporal segment of the input voice signal associated with a language-independent acoustic-phonetic class comprises;

adapting the original acoustic model of each of said language-independent acoustic-phonetic classes to the speaker, based on the set of observation vectors associated with the temporal segment of the input voice signal in turn associated with the language-independent acoustic-phonetic class.

32. The method of claim 31, wherein the original acoustic model of each of said language-independent acoustic-phonetic classes is formed by a number of acoustic states, and wherein adapting the original acoustic model of each of said language-independent acoustic-phonetic classes to the speaker, based on the set of observation vectors associated with the corresponding temporal segment of the input voice signal, comprises:

associating sub-sets of observation vectors in said set of observation vectors with corresponding acoustic states of the original acoustic model of said language-independent acoustic-phonetic class; and

adapting each acoustic state of the original acoustic model of said language-independent acoustic-phonetic class to the speaker, based on the corresponding sub-set of observation vectors.

33. The method of claim 32, wherein adaptation of an original acoustic model of a language-independent acoustic-phonetic class to a speaker is performed by implementing a maximum a posteriori adaptation technique.

34. The method of claim 32, wherein association of sub-sets of observation vectors with acoustic states of said original acoustic models of said language-independent acoustic-phonetic classes is carried out by means of dynamic programming techniques which perform dynamic time-warping based on said original acoustic models.

35. A method for verifying a speaker based on a voice-print created according to claim 27, and on an input voice signal representing an utterance of said speaker, comprising:

processing said input voice signal to provide a sequence of language-independent acoustic-phonetic classes associated with corresponding temporal segments of said input voice signal; and

computing a likelihood score indicative of a probability that said utterance has been made by the same speaker as the speaker to whom said voice-print belongs, said likelihood score being computed based on said input speech signal, said original acoustic models of said language-independent acoustic-phonetic classes and the adapted acoustic models of said language-independent acoustic-phonetic classes used to create said voice-print.

36. The method of claim 35, wherein said language-independent acoustic-phonetic classes are represented by respective original acoustic models having the same topology as the original acoustic models used to create said voice-print.

37. The method of claim 35, wherein computing said likelihood score comprises:

computing first contributions to said likelihood score, one for each one of said language-independent acoustic-phonetic classes, each first contribution being computed based on a corresponding temporal segment of said input voice signal, and on the adapted acoustic model of said language-independent acoustic-phonetic class used to create said speaker voice-print;

computing second contributions to said likelihood score, one for each language-independent acoustic-phonetic class, each second contribution being computed based on a corresponding temporal segment of said input voice signal, and on the original acoustic model of said language-independent acoustic-phonetic class; and

computing said likelihood score based on said first and second contributions.

38. The method of claim 36, wherein processing said input voice signal comprises:

extracting observation vectors from said input voice signal, each observation vector being formed by parameters extracted from the input voice signal at a fixed time frame;

temporally aligning said observation vectors with said input voice signal so as to associate sets of observation vectors with corresponding temporal segments of the input voice signal;

wherein computing a first contribution to said likelihood score for each language-independent acoustic-phonetic class comprises;

computing said first contribution to said likelihood score based on a set of observation vectors associated with the language-independent acoustic-phonetic class and the adapted acoustic model of said language-independent acoustic-phonetic class used to create said speaker voice-print;

and wherein computing said second contribution to said likelihood score for each language-independent acoustic-phonetic class comprises;

computing said second contribution to said likelihood score based on the set of observation vectors associated with said language-independent acoustic-phonetic class and said original acoustic model of said language-independent acoustic-phonetic class.

39. The method of claim 35, further comprising:

verifying said speaker based on said likelihood score.

40. The method of claim 39, wherein verifying said speaker comprises:

comparing said likelihood score with a given threshold; and

verifying said speaker based on an outcome of said comparison.

41. The method of claim 35, wherein processing said input voice signal comprises:

carrying out a neural network-based decoding.

42. The method of claim 41, wherein said neural network-based decoding is performed by using a hybrid hidden Markov models/artificial neural networks decoder.

43. The method of claim 35, wherein said original acoustic models of said language-independent acoustic-phonetic classes are hidden Markov models.

44. A method for identifying a speaker based on a number of voice-prints, each created according to claim 27, and on an input voice signal, representing an utterance of said speaker, comprising:

performing a number of speaker verifications according to a method for verifying a speaker based on a voice-print created according to the method of claim 27, and on an input voice signal representing an utterance of said speaker, comprising;

processing said input voice signal to provide a sequence of language-independent acoustic-phonetic classes associated with corresponding temporal segments of said input voice signal; and

computing a likelihood score indicative of a probability that said utterance has been made by the same speaker as the speaker to whom said voice-print belongs, said likelihood score being computed based on said input speech signal, said original acoustic models of said language-independent acoustic-phonetic classes and the adapted acoustic models of said language-independent acoustic-phonetic classes used to create said voice-print, each speaker verification being based on a respective one of said voice-prints; and

identifying said speaker based on outcomes of said speaker verifications.

45. The method of claim 44, wherein each speaker verification provides a corresponding likelihood score, and identifying said speaker based on outcomes of said speaker verifications comprising:

identifying said speaker based on said likelihood scores.

46. The method of claim 45, wherein identifying said speaker based on said likelihood scores comprises:

identifying the maximum likelihood score;

comparing said maximum likelihood score with a given threshold; and

identifying said speaker based on an outcome of said comparison.

47. A speaker recognition system capable of being configured to implement a method for creating a voice-print of a speaker based on an input voice signal representing an utterance of said speaker, comprising:

processing said input voice signal to provide a sequence of language-independent acoustic-phonetic classes associated with corresponding temporal segments of said input voice signal, said language-independent acoustic-phonetic classes representing sounds in said utterance and being represented by respective original acoustic models;

adapting the original acoustic model of each of said language-independent acoustic-phonetic classes to the speaker, based on the temporal segment of the input voice signal associated with a language-independent acoustic-phonetic class; and

creating said voice-print based on the adapted acoustic models of said language-independent acoustic-phonetic classes.

48. The system of claim 47, capable of being further configured to implement a method for verifying a speaker based on a voice-print created according to the method for creating a voice-print of a speaker and on an input voice signal representing an utterance of said speaker, comprising:

processing said input voice signal to provide a sequence of language-independent acoustic-phonetic classes associated with corresponding temporal segments of said input voice signal; and

computing a likelihood score indicative of a probability that said utterance has been made by the same speaker as the speaker to whom said voice-print belongs, said likelihood score being computed based on said input speech signal, said original acoustic models of said language-independent acoustic-phonetic classes, and the adapted acoustic models of said language-independent acoustic-phonetic classes used to create said voice-print.

49. The system of claim 47, capable of being further configured to implement a method for identifying a speaker based on a number of voice-prints, each created according to the method for creating a voice-print of a speaker, and on an input voice signal, representing an utterance of said speaker, comprising:

performing a number of speaker verifications by a method for verifying a speaker based on a voice-print created according to the method for creating a voice-print of a speaker and on an input voice signal representing an utterance of said speaker, comprising;

processing said input voice signal to provide a sequence of language-independent acoustic-phonetic classes associated with corresponding temporal segments of said input voice signal; and

computing a likelihood score indicative of a probability that said utterance has been made by the same speaker as the one to whom said voice-print belongs, said likelihood score being computed based on said input speech signal, said original acoustic models of said language-independent acoustic-phonetic classes, and the adapted acoustic models of said language-independent acoustic-phonetic classes used to create said voice-print, each speaker verification being based on a respective one of said voice-prints; and

identifying said speaker based on outcomes of said speaker verifications.

50. A computer program product loadable in a memory of a processing system and comprising software code portions capable of implementing, when the computer program product is run on the processing system, a method for creating a voice-print of a speaker based on an input voice signal representing an utterance of said speaker, comprising:

processing said input voice signal to provide a sequence of language-independent acoustic-phonetic classes associated with corresponding temporal segments of said input voice signal, said language-independent acoustic-phonetic classes representing sounds in said utterance and being represented by respective original acoustic models;

adapting the original acoustic model of each of said language-independent acoustic-phonetic classes to the speaker, based on the temporal segment of the input voice signal associated with a language-independent acoustic-phonetic class; and

creating said voice-print based on the adapted acoustic models of said language-independent acoustic-phonetic classes.

51. The computer program product of claim 50, further comprising software code portions capable of implementing, when the computer program product is run on the processing system, a method for verifying a speaker based on a voice-print created according to the method for creating a voice-print of a speaker and on an input voice signal representing an utterance of said speaker, comprising:

processing said input voice signal to provide a sequence of language-independent acoustic-phonetic classes associated with corresponding temporal segments of said input voice signal; and

computing a likelihood score indicative of a probability that said utterance has been made by the same speaker as the speaker to whom said voice-print belongs, said likelihood score being computed based on said, input speech signal, said original acoustic models of said language-independent acoustic-phonetic classes, and the adapted acoustic models of said language-independent acoustic-phonetic classes used to create said voice-print.

52. The computer program product of claim 50, further comprising software code portions capable of implementing, when the computer program product is run on the processing system, a method for identifying a speaker based on a number of voice-prints, each created according to the method for creating a voice-print of a speaker, and on an input voice signal representing an utterance of said speaker, comprising:

performing a number of speaker verifications by a method for verifying a speaker based on a voice-print created according to the method for creating a voice-print of a speaker and on an input voice signal representing an utterance of said speaker, comprising;

processing said input voice signal to provide a sequence of language-independent acoustic-phonetic classes associated with corresponding temporal segments of said input voice signal; and

computing a likelihood score indicative of a probability that said utterance has been made by the same speaker as the speaker to whom said voice-print belongs, said likelihood score being computed based on said, input speech signal, said original acoustic models of said language-independent acoustic-phonetic classes, and the adapted acoustic models of said language-independent acoustic-phonetic classes used to create said voice-print, each speaker verification being based on a respective one of said voice-prints; and

identifying said speaker based on outcomes of said speaker verifications.