1. 1 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Patrol Team Language Identification System for DARPA RATS P1 Evaluation Pavel Matejka 1, Oldrich Plchot 1, Mehdi Soufifar 1, Ondrej Glembek 1, Luis Fernando D’Haro 1, Karel Vesely 1, Frantisek Grezl 1, Jeff Ma 2, Spyros Matsoukas 2, and Najim Dehak 3 1 Brno University of Technology, Speech@FIT and IT4I Center of Excellence, Czech 2 Raytheon BBN Technologies, Cambridge, MA, USA 3 MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA matejkap@fit.vutbr.cz, smatsouk@bbn.com, najim@csail.mit.edu

2. 2 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Outline  About DARPA RATS program  Datasets and task description  Subsystems with analysis  Fusion and Results  Conclusion

3. 3 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka DAPRA RATS Program  RATS = Robust Automatic Trascription of Speech  Goal : create algorithms and software for performing the following tasks on speech-containing signals received over communication channels that are extremely noisy and/or highly distorted.  Tasks : –Speech Activity Detection –Keyword Spotting –Language Identification –Speaker Identification  Data collector : LDC  Evaluation by SAIC  Performer: PATROL Team led by BBN

4. 4 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Data Specification  Languages: –Dari, Levantine Arabic, Urdu, Pashtu, Farsi –>10 out of set languages  Durations: 120s, 30s, 10s, 3s  Telephone conversations retransmitted over 8 noisy radio communication channels [marked as A-H]  Available: collections of 2-min audio samples –LDC2011E95 – split to train and dev by SAIC –LDC2011E111 – split to train and dev by Patrol team –LDC2012E03 – supplemental training for non-target languages  The amount of audio data for different languages heavily unbalanced  Added shorter duration samples –Derived from 2-min samples, based on our SAD output

5. 5 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Datasets  Train –Main Only files where VAD detects >60s of speech 30774 files together Unbalanced = 668 files for Dari, 12778 for Leventine Arabic –Balanced Balanced over files for each language and channel 7150 files for each duration 673 files for Dari, otherwise ~1300 –Extended Main + all 30sec cuts from Main set + entire LDC2012e03 (only nontarget languages) ~170k segments  Development Set –Corpus was driven by Dari - only 679 source files, other languages limited to 1000 files, 2432 files for non target languages –~7120 files for each duration  Evaluation Data –2527 files for each duration

6. 6 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka LID Patrol System Architecture Audio CZ Phoneme Recognition Phonotactic iVector LID iVector LID JFA LID BBN SAD iVector LID Combined Score BUT SAD Calibration & Fusion

7. 7 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Speech Activity Detection  One of the most important blocks since the data really difficult –See separate paper about SAD development on Wednesday 16:00 in Pavilon West  Used both GMM-based (BBN) and MLP-based (BUT) detectors.

8. 8 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Speech Activity Detection  Comparison of different SAD systems –Robust SAD tuned for noisy telephone speech –Robust SAD tuned for RATS  Results (Cavg) are on DEV set (but scored with SRC channel)  iVector system (600dim) used for this experiment  25% relative gain SAD type/ Cavg[%]120s Telephone2,2 RATS1,6

9. 9 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka iVector LID System (BUT)  Acoustic Features –Dithering, bandwidth 300-3200Hz for 25 Mel-filters, 6 MFCC+C0 –CMN/CVN (based on SAD), RASTA normalization –Shifted Delta Cepstra (SDC) 7-1-3-1  UBM –Language independent, diagonal-covariance, 2048 Gaussians –Trained on balanced train set  iVector –600 dimensions –Trained on main set  Neural network classifier –iVector input, 6 outputs (1 nontarget + 5 target languages) –Hidden layer with 200 nodes –Stochastic Gradient Descent training with L2 regularization –Trained on extended set (all data + all 30 sec splits)

10. 10 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Comparison of Logistic Regression and Neural Network as final classifier  BUT iVector system (600dim)  Results on Development set  Logistic Regression  trained by: Trusted Region Conjugate-GD  Results on Development set  Neural Net:  one hidden layer 200  trained by: Stochastic-GD with L2 regularization  also experiments with Conjugate-GD, but no improvement

11. 11 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka JFA LID System (BUT)  Acoustic Features –Same as for iVector system + Wiener filtering  Universal Background Model (UBM) –Language independent, –Diagonal-covariance, 2048 Gaussians –Trained on balanced train set  JFA –Trained on main train set –μ = m + Dz + Ux –Models of languages D are MAP adapted from UBM with tau =10 –Channel matrix U with 200 dimensions –Linear scoring

12. 12 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Importance of Wiener Filter  400dim i-vector + logistic regression experimental system  Results on Development set

13. 13 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka iVector LID System (BBN)  Acoustic Features –RASTA-PLP –Block of 11-frame PLPs, projected to 60 dimensions via HLDA  UBM –Language dependent (5 target, 1 “non-target”), 1024 Gaussians  iVector –400 dimensions –Group adjacent speech segments into 20s chunks, estimate one iVector per chunk improves performance on short duration conditions by 28% –Estimate 6 iVectors (one per UBM) –Apply neural network (NN) to each iVector - 6 outputs (1 nontarget + 5 target languages) –Combine NN outputs to form 6-dimensional score vector 26% relative improvement compared to using language independent i-vectors

14. 14 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Analysis of iVector LID System (BBN) Analysis of the BBN iVector extractor training and UBM: 1.Whole audio segments, single UBM 2.Audio split to 20s segments, single UBM 3.Audio split to 20s segments, language dependent background models (LDBM)

15. 15 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Phonotactic iVector LID System (BUT)  Phoneme recognizer –Czech CTS recognizer trained on artificially noised data Added noise with varying SNR (lowest 10dB) to 30% of the corpus –38 phonemes –3-gram counts: sum of posterior probabilities of 3-grams from phone lattices  iVector – Multinomial subspace modeling –600 dimensions, trained on main train set –Training a low-dimensional subspace in the framework of total variability model using multinomial distribution –Using point-estimate of the model’s latent variable for each utterance as our new features  Logistic regression as final classifier –Trained on main train set

16. 16 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Fusion and Calibration  Regularized logistic regression –Objective: minimize cross-entropy on development set –Duration-independent – trained on files from 10s, 30s, and 120s conditions  Procedure –Calibrate (tune) each system individually –Combine calibrated system outputs into a single output vector Fusion parameters estimated on the same development set  Performance evaluation –Primarily Cavg score –Also computed P MISS and P FA at Phase 1 target operating points

17. 17 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Overall Results

18. 18 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Robustness  There is channel B completely removed from the training of the contrastive system (noB) (channel B is unseen channel)  Results on Development set with BUT iVector system (600dim)  Over all results System/Cavg[%]120s30s10s3s iVector NN3.49.716.825.8 iVector NN noB8.114.719.829.0 System/Cavg[%]120s30s10s3s iVector NN1.96.613.023.6 iVector NN noB2.57.213.423.8  Results only for channel B

19. 19 Patrol LID System for DARPA RATS P1 Evaluation Pavel Matejka Conclusion  SAD is crucial  De-noising helps  Benefit from using Language dependent UBM  Benefit from using NN as final classifier for LID