SPEAKER RECOGNITION BASED ON DISCRIMINANT ANALYSIS
A method for speaker recognition, an electronic device and a speaker recognition system are disclosed. An example method includes receiving speech data corresponding to one or more utterances from a plurality of speakers that include a plurality of voice features. A plurality of variability factors is extracted from the speech data. The dimensionality of the plurality of variability factors is reduced using a non parametric analysis, thereby generating dimensionality reduced features. A score space is defined based at least on the dimensionality reduced features.
1 . A method for speaker recognition, comprising:
receiving speech data corresponding to one or more utterances from a plurality of speakers that include a plurality of voice features;
extracting a plurality of variability factors from the speech data;
reducing dimensionality of the plurality of variability factors using a non-parametric analysis, thereby generating dimensionality reduced features; and
defining a score space based at least on the dimensionality reduced features.
2 . The method of claim 1 , wherein the variability factors include speaker-dependent factors and session-dependent factors.
3 . The method of claim 1 , further comprising:
receiving subsequent speech data from a target speaker;
scoring multiple variability factors of the target speaker using the score space; and
identifying the target speaker based at least on a score of the multiple variability factors.
4 . The method of claim 1 , wherein the non-parametric analysis is a Nearest Neighbor Discriminant Analysis (NNDA).
5 . The method of claim 4 , further comprising using a nearest neighbor rule which maintains within-class and between-class variations of the plurality of variability factors to reduce dimensionality.
6 . The method of claim 1 , comprising defining the score space using a probabilistic discriminant analysis of the dimensionality reduced features.
7 . The method of claim 1 , comprising extracting the plurality of variability factors using a total variability matrix trained by a Universal Background Model (UBM) trained by a Gaussian Mixture Model (GMM).
8 . The method of claim 7 , wherein the total variability matrix is further trained using Baum-Welch statistics of the plurality of voice features.
9 . The method of claim 1 , wherein the plurality of voice features are determined using Mel frequency cepstral coefficients (MFCC).
10 . An electronic device comprising:
an extractor configured to extract a plurality of variability factors from speech data; and
an analyzer configured to reduce dimensionality of the plurality of variability factors using a non-parametric analysis, thereby generating dimensionality reduced features, and define a score space using a probabilistic discriminant analysis on the dimensionality reduced features.
11 . The electronic device of claim 10 , further comprising a scorer configured to:
receive, from the extractor, multiple variability factors extracted from subsequently received speech data of a target speaker;
score at the multiple variability factors of the target speaker using the score space; and
identify the target speaker based at least on a score of the multiple variability factors.
12 . The electronic device of claim 10 , wherein the analyzer is configured to reduce dimensionality using a Nearest Neighbor Discriminant Analysis (NNDA).
13 . The electronic device of claim 10 , wherein the analyzer is configured to define the score space using a probabilistic discriminant analysis of the dimensionality reduced features.
14 . A computer-readable medium having computer-executable instructions stored thereon that, when executed by a computer, cause the computer to perform corresponding functions, the functions comprising:
receiving speech data corresponding to one or more utterances from a plurality of speakers that include a plurality of voice features;
extracting a plurality of variability factors from the speech data;
reducing dimensionality of the plurality of variability factors using a non-parametric analysis, thereby generating dimensionality reduced features; and
defining a score space based at least on the dimensionality reduced features.
15 . The computer-readable medium of claim 14 , wherein the instructions further comprise instructions that, when executed by the computer, cause the computer to perform corresponding functions, the functions comprising:
receiving subsequent speech data from a target speaker;
scoring multiple variability factors of the target speaker using the score space; and
identifying the target speaker based at least on a score of the multiple variability factors.
16 . The computer-readable medium of claim 14 , wherein the instructions further comprise instructions that, when executed by the computer, cause the computer to perform corresponding functions, the functions comprising reducing dimensionality by computing local sample averages of a number of samples in a neighborhood of each individual sample of the plurality of variability factors.
17 . The computer-readable medium of claim 14 , wherein the instructions further comprise instructions that, when executed by the computer, cause the computer to perform corresponding functions, the functions comprising defining the score space using a probabilistic discriminant analysis of the dimensionality reduced features.
18 . The computer-readable medium of claim 14 , wherein the instructions further comprise instructions that, when executed by the computer, cause the computer to perform corresponding functions, the functions comprising extracting the plurality of variability factors using a total variability matrix trained by a Universal Background Model (UBM) trained by a Gaussian Mixture Model (GMM).
19 . The computer-readable medium of claim 14 , wherein the variability factors include speaker-dependent factors and session-dependent factors.
20 . The computer-readable medium of claim 14 , wherein the non-parametric analysis is a Nearest Neighbor Discriminant Analysis (NNDA).