System and method for speaker verification using short utterance enrollments

• US 7,490,043 B2
• Filed: 02/07/2005
• Issued: 02/10/2009
• Est. Priority Date: 02/07/2005
• Status: Expired due to Fees

• Current Assignee: Hitachi, Ltd.
• Original Assignee: Hitachi, Ltd.
• Inventors: Tavares, Clifford
• Primary Examiner(s): MCFADDEN, SUSAN IRIS
• Application Number:
  US11/052,944
• Publication Number:
  US 20060178885A1
• Time in Patent Office: 1,464 Days
• Field of Search: 704/273, 382/115
• US Class Current: 704/273
• CPC Class Codes: G10L 17/08 Use of distortion metrics or a particular distance between probe pattern and reference templates

Abstract

In a system and method verifying an identity of a speaker, feature vectors are received that were extracted from an utterance made by a speaker claiming an identity. Dissimilarity may be measured between the feature vectors and a codebook associated with a version of the utterance known to be made by the identity. The utterance may be further analyzed to ascertain information about repeating occurrences of the feature vectors in the utterance. The information about repeating occurrences of feature vectors occurring in the utterance may be compared to information about repeating occurrences of feature vectors in a version of the utterance known to be made by the claimed identity. Based on the comparison, a penalty may be assigned to the measured dissimilarity. Using the measured dissimilarity modified by the assigned penalty, a determination may be made as to whether to accept the speaker as the identity.

Claims

1. A method for verifying an identity of a speaker, comprising:

receiving feature vectors extracted from an utterance received from a microphone and made by a speaker claiming an identity;

measuring dissimilarity between the feature vectors and a codebook associated with a version of the utterance known to be made by the identity;

analyzing the utterance to ascertain information about repeating occurrences of the feature vectors in the utterance;

comparing the information about repeating occurrences of feature vectors occurring in the utterance with information about repeating occurrences of feature vectors in a version of the utterance known to be made by the claimed identity;

assigning a penalty to the measured dissimilarity based on the comparison; and

determining whether to accept the speaker as the identity using the measured dissimilarity modified by the assigned penalty, wherein if the number of repeating occurrences for any of the feature vectors of the utterance exceeds a maximum number, rejecting the speaker as the identity, and wherein an additional penalty is assigned to the dissimilarity if any of the feature vectors of the utterance is determined to have a number of repeating occurrences exceeding the maximum number.

2. The method of claim 1, wherein the determining whether to accept the speaker as the identity further comprises comparing the measured dissimilarity modified by the assigned penalty to a valid-imposter model associated with the utterance.

3. The method of claim 1, wherein the utterance comprises a plurality of frames, and wherein analyzing the utterance further comprises identifying the feature vectors occurring in each frame, counting the instances that each different feature vector of the utterance occurs in all of the frames to obtain a sum of repeating occurrences of each feature vector, and dividing each sum by a total number of repeating occurrences occurring in the utterance.

4. The method of claim 1, wherein an additional penalty is assigned to the dissimilarity if total number of the feature vectors with repeating occurrences exceeding the maximum number exceeds a predetermined number.

5. The method of claim 1, wherein an additional penalty is assigned to the dissimilarity if total number of the feature vectors with repeating occurrences exceeds a predetermined number.

6. A method for verifying an identity of a speaker, comprising:

receiving feature vectors extracted from an utterance received from a microphone and made by a speaker claiming an identity;

measuring dissimilarity between the feature vectors and a codebook associated with a version of the utterance known to be made by the identity;

analyzing the utterance to ascertain information about repeating occurrences of the feature vectors in the utterance;

comparing the information about repeating occurrences of feature vectors occurring in the utterance with information about repeating occurrences of feature vectors in a version of the utterance known to be made by the claimed identity;

assigning a penalty to the measured dissimilarity based on the comparison; and

determining whether to accept the speaker as the identity using the measured dissimilarity modified by the assigned penalty, wherein if the number of repeating occurrences for any of the feature vectors of the utterance exceeds a maximum number, rejecting the speaker as the identity, and wherein the maximum number for a given feature vector is obtained by analyzing a plurality of utterances made by a plurality of speakers to identify the utterance of the plurality of utterances having the largest number of repeating occurrences of the given feature vector.

7. A method for verifying an identity of a speaker, comprising:

receiving feature vectors extracted from an utterance received from a microphone and made by a speaker claiming an identity;

measuring dissimilarity between the feature vectors and a codebook associated with a version of the utterance known to be made by the identity;

analyzing the utterance to ascertain information about repeating occurrences of the feature vectors in the utterance;

comparing the information about repeating occurrences of feature vectors occurring in the utterance with information about repeating occurrences of feature vectors in a version of the utterance known to be made by the claimed identity;

assigning a penalty to the measured dissimilarity based on the comparison; and

determining whether to accept the speaker as the identity using the measured dissimilarity modified by the assigned penalty, wherein the assigned penalty comprises a penalty for each of the feature vectors of the utterance, wherein the assigned penalty for each feature vector is based on a difference between a number of repeating occurrences of the respective feature vector of the utterance and a number of repeating occurrences of the corresponding feature vector of the version of the utterance know to be made by the identity.

8. The method of claim 7, wherein the assigned penalty for a given feature vector is reduced if the difference between the number of repeating occurrences of the respective feature vector of the utterance and the number of repeating occurrences of the corresponding feature vector of the version of the utterance know to be made by the identity is determined to be less than a predefined value.

9. The method of claim 7, wherein the assigned penalty for given feature vector is adjusted based on the degree of difference between the number of repeating occurrences of the respective feature vector of the utterance and the number of repeating occurrences of the corresponding feature vector of the version of the utterance know to be made by the identity.

10. The method of claim 7, wherein the assigned penalty for each feature vector is adjusted to account for operational characteristics of a device used to capture the utterance by the speaker.

11. The method of claim 7, wherein no penalty is assigned to a given feature vector if the difference between the number of repeating occurrences of the respective feature vector of the utterance and the number of repeating occurrences of the corresponding feature vector of the version of the utterance know to be made by the identity is determined to be less than an expected difference of repeating occurrences occurring due to expected changes in voice.