The next set of experiments we present is related to the i–vector extraction process. In particular, we compare the performance of diagonalized i–vector, VB–based i– vector and CG–based i–vector extractors (Sections3.5.3) in terms of extraction time and performance of the generated i–vectors. In order to asses i–vector quality, we present the results of a WCCN–LDA–cosine distance system and a GPLDA with WCCN and length normalization system. 400–dimensional i–vectors are extracted from a 60–MFCC 2048–Gaussians diagonal covariance GI UBM trained over NIST 2004, 2005 and 2006 SRE data [133, 134, 135]. The i–vector subspace matrix was estimated using the same data as for the UBM. LDA–WCCN system and GPLDA were trained using the NIST SRE 2004, 2005 and 2006 train and test corpora, the Switchboard II Phase 2 and 3 corpora and the Switchboard Cellular part 1 and 2 corpora [123]. Accuracy results are given in terms of EER, 2008 NIST SRE 2008 DCF (DCF08) [140] and NIST 2010 SRE DCF (DCF10) [120] for the female subset of the NIST 2010 SRE tel–tel condition [120]. Timings do not include feature extraction and statistics estimation.
The first two lines show the performance, average extraction time and required memory for the classical i–vector extraction approaches. The fast method pre- computes the TT
cΣ−1c Tc matrices, while the slow method corresponds to the naïve
posterior calculations. The third line presents the results of diagonalized i–vectors where the diagonalizing matrix is estimated as described in Section 3.5.3. We can observe that this technique has very fast extraction time with very limited memory requirements, however recognition accuracy is degraded significantly. The next two lines refer to a VB implementation using a block size of 20, and differ only in the number of iterations required. In particular, we used a stopping criterion based on the norm of the difference between two consecutive updates. The number shown in the system name column represents the stopping threshold. While not as fast as diagonalized i–vectors, the VB approach allows very good performance in terms of speed–up with almost no accuracy degradation. Finally, the last two lines refer to the CG–based i–vector extraction. In this case, the stopping criterion is given by the norm of the residual term and the number next to the system names shows the corresponding threshold. As for the VB, also the CG proves to be a fast and accurate technique for i–vector extraction. While slightly inferior to the VB in terms of processing time, this technique allows fast estimation of accurate i–vectors with the same memory requirements as diagonalized i–vectors.
Table 6.15: Results for the NIST SRE2010 tests in terms of % EER, minDCF08 and minDCF10 with different i–vector extractors
1 core 12 cores
System Memory 100 utterances 500 utterances
1127224 frames 6168082 frames
(MB) rel. speedup cpu time rel. speedup cpu time
Fast baseline 2875 1 116.8 s 1 109.2 s Slow baseline 375 0.27 435.9 s 0.36 306.5 s Eigen 382 29.30 4.0 s 12.71 8.8 s VB-20-2 500 4.23 27.5 s 2.98 37.5 s VB-20-1 500 2.63 44.3 s 1.60 69.5 s CG-2 375 0.85 136.6 s 1.48 75.4 s CG-1 375 0.56 207.4 s 1.01 110.4 s
System Cosine Scoring PLDA
EER (%) DCF08 DCF10 EER (%) DCF08 DCF10 Fast baseline 4.97 230 612 3.59 180 567 Slow baseline 4.97 230 612 3.59 180 567 Eigen 5.67 252 697 4.26 202 685 VB-20-2 5.13 232 622 3.51 183 576 VB-20-1 4.93 229 621 3.46 182 569 CG-2 5.16 224 618 3.59 183 567 CG-1 4.96 230 612 3.59 179 564
Conclusions
We presented an overview of several state–of–the–art technologies for speaker and language recognition, with a particular focus on Support Vector Machine based discriminative techniques. We analyzed some techniques to extract significant fea- tures from the acoustic signal and to model the speech content by means of pho- netic decoding. We also detailed some of the basic models which are the basis of many different speaker and language systems, Gaussian Mixture Models and Hidden Markov Models. We then focused our attention on latent variable models, and in particular on the use of Factor Analysis derived techniques to build information–rich low–dimensional representations of utterances. We then described state–of–the–art generative techniques built on top of these low–dimensional representations.
We also presented the author’s contributions to these fields, which cover different aspects of speaker and language modeling, starting from a technique to speed–up neural network training for speech decoding by means of a GPGPU computing framework, technique which allowed us to greatly improve training times.
As far as JFA models are concerned, we detailed a technique for discriminative language recognition using low–dimensional features extracted from a Factor Anal- ysis front–end, the language factors. We proved that these low–dimensional features can be applied to different language recognition schemes and that they allow achiev- ing good recognition performance while, at the same time, training simplifies thanks to the reduced size of patterns.
Still working with low–dimensional features, we showed an efficient way to com- pute i–vectors based on variational approximations of distributions. Experimental results show that these techniques allow for much faster i–vector extraction with low memory requirements while preserving recognition accuracy. Variational approxi- mations, moreover, also provide a framework to extend i–vectors to use different prior and posterior distributions.
Finally, we analyzed discriminative techniques based on Support Vector Ma- chines (SVM). We presented some of the concepts and issues related to SVM train- ing, together with some solutions for large–scale linear problems. We then applied SVMs to both language recognition and speaker recognition problems. In the former case, we showed how different algorithms behave with different language recognition techniques. We also showed how multiclass classifiers can be used in a phonotactic language recognition system based on low–dimensional representations of phonetic information. We then presented a novel framework for discriminative training of speaker verification systems, Pairwise SVMs (PSVM). We showed that pairwise SVMs provide a more natural approach to discriminative speaker verification com- pared to the classical one–vs–all paradigm. We also showed that this technique has strong connections with state–of–the–art generative models and, at the same time, can be interpreted as a simple polynomial kernel classifier. While some issues are still open, for example extensions of the model to deal with more than two utter- ances or large–scale training, pairwise SVMs provide models which allow for fast scoring of test utterances and achieve state–of–the–art performance.
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