I-Vector Based Clustering Training Data in Speech Recognition

• US 20150199960A1
• Filed: 08/24/2012
• Published: 07/16/2015
• Est. Priority Date: 08/24/2012
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Huo, Qiang, Yan, Zhi-Jie, Zhang, Yu, Xu, Jian
• Application Number:
  US13/640,804
• Publication Number:
  US 20150199960A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/14 using statistical models, e.g. Hidden Markov Models [HMMs] G10L15/18 takes precedence
• CPC Class Codes: G10L 2015/0631 Creating reference templates; Clustering

Abstract

Methods and systems for i-vector based clustering training data in speech recognition are described. An i-vector may be extracted from a speech segment of a speech training data to represent acoustic information. The extracted i-vectors from the speech training data may be clustered into multiple clusters using a hierarchical divisive clustering algorithm. Using a cluster of the multiple clusters, an acoustic model may be trained. This trained acoustic model may be used in speech recognition.

Claims

1. A computer-implemented method for clustering training data in speech recognition, the method comprising:

extracting a plurality of i-vectors from speech data including a plurality of speech segments;

clustering the plurality of i-vectors into a plurality of clusters;

training an acoustic model using one of the plurality of clusters; and

recognizing one or more other speech segments using the trained acoustic model.

2. The computer-implemented method as recited in claim 1, wherein

the extracting the plurality of i-vectors from the speech data comprises:

training a Gaussian mixture model (GMM) to represent the speech data;

calculating a set of hyperparameters based on the speech data; and

extracting the plurality of i-vectors based on the GMM and the set of hyperparameters.

3. The computer-implemented method as recited in claim 2, wherein

the calculating the set of hyperparameters comprises:

initializing the set of hyperparameters;

calculating statistics corresponding to the plurality of speech segments;

calculating a posterior expectation associated with the speech data using;

the one or more corresponding statistics, and

the set of hyperparameters; and

updating the set of hyperparameters based on the posterior expectation to generate an updated set of hyperparameters, wherein the extracting the i-vector is further based on the updated set of hyperparameters.

4. The computer-implemented method as recited in claim 2, further comprising:

calculating an additional set of hyperparameters using a residual term to model variabilities associated with the speech data that are not captured by the set of hyperparameters, and wherein the extracting the i-vector is further based on the additional set of hyperparameters.

5. The computer-implemented method as recited in claim 1, wherein a similarity between two i-vectors of the plurality of i-vectors is measured using one of a Euclidean distance or a cosine measure.

6. The computer-implemented method as recited in claim 1, wherein the acoustic model is cluster-dependent and trained based on a cluster-independent acoustic model that is trained using speech data.

7. The computer-implemented method as recited in claim 6, wherein the recognizing the one or more speech segments using the trained acoustic model comprises recognizing the one or more speech segments using the cluster-dependent acoustic model and the cluster-independent acoustic model.

8. The computer-implemented method as recited in claim 1, further comprising:

receiving other speech data;

generating the one or more other speech segments based on the other speech data;

extracting an i-vector from one segment of the one or more other speech segments;

selecting a cluster corresponding to the i-vector; and

determining an acoustic model that is trained by the cluster, and wherein the recognizing the one or more other speech segments using the trained acoustic model comprises recognizing the one segment using the acoustic model.

9. A method comprising:

under control of one or more computing systems comprising one or more processors,

receiving speech data including a plurality of speech segments;

extracting an i-vector from a speech segment of the plurality of speech segments;

selecting a cluster corresponding to the i-vector; and

determining an acoustic model corresponding to the cluster; and

recognizing the speech segment using the acoustic model.

10. The method as recited in claim 9, further comprising:

extracting a plurality of i-vectors from a plurality of training speech segments;

clustering the plurality of i-vectors into multiple clusters that includes the cluster; and

training acoustic models using the multiple clusters, the acoustic models including the acoustic model.

11. The method as recited in claim 10, wherein the extracting the plurality of i-vectors from the plurality of training speech segments comprises:

training a GMM based on the plurality of training speech segments;

calculating hyperparameters of the plurality of training speech segments;

calculating additional hyperparameters to model variabilities of the plurality of training speech segments not captured by the hyperparameters; and

extracting the plurality of i-vectors based on the GMM, the hyperparameters and the additional hyperparameters.

12. The method as recited in claim 9, wherein the selecting the cluster corresponding to the i-vector comprises:

normalizing the i-vector using a cosine similarity measure; and

selecting the cluster based on a similarity between the i-vector and a centroid of the cluster.

13. The method as recited in claim 12, wherein the selecting the cluster comprises selecting multiple clusters based on similarities between the i-vector and centroids of the multiple clusters, and wherein the determining the acoustic model corresponding to the cluster comprises determining multiple acoustic models corresponding to the multiple clusters.

14. The method as recited in claim 9, wherein the determining the acoustic model comprises determining a cluster-dependent acoustic model and a cluster-independent acoustic model, and wherein the cluster-dependent acoustic model is trained based on the cluster-independent acoustic model.

15. One or more computer-readable media storing instructions that are executable by one or more processors to perform acts comprising:

receiving a plurality of training speech segments;

extracting multiple i-vectors from the plurality of training speech segments based on a set of hyperparameters of the plurality of training speech segments, individual ones of the i-vectors of the multiple i-vectors corresponding to a training speech segment of the plurality of training speech segments;

clustering the i-vectors into multiple clusters;

training a cluster-dependent acoustic model using a cluster of the multiple clusters; and

recognizing an unknown speech segment using the cluster-dependent acoustic model.

16. The one or more computer-readable media as recited in claim 15, wherein an i-vector extracted from the unknown speech segment is associated with a cluster corresponding to the cluster-dependent acoustic model.

17. The one or more computer-readable media as recited in claim 15, wherein the extracting multiple i-vectors comprises extracting multiple i-vectors further based on an additional set of hyperparameters that model variabilities of the plurality of training speech segments not captured by the set of hyperparameters.

18. The one or more computer-readable media as recited in claim 15, wherein the set of hyperparameters are determined based on Baum-Welch statistics that correspond to the plurality of training speech segments and a GMM that is trained to represent the plurality of training speech segments.

19. The one or more computer-readable media as recited in claim 15, wherein the clustering the i-vectors into multiple clusters comprises clustering the i-vectors into multiple clusters using a Linde-Buzo-Gray (LBG) algorithm.

20. The one or more computer-readable media as recited in claim 15, wherein a similarity between two i-vectors of the multiple i-vectors is measured using one of a Euclidean distance or a cosine measure.