bob.spear 1.1.1

1. Voice Activity Detection

This step aims to filter out the non speech part. Depending on the configuration file, several routines can be enabled or disabled.

• Energy-based VAD

• 4Hz Modulation energy based VAD

2. Feature Extraction

This step aims to extract features. Depending on the configuration file, several routines can be enabled or disabled.

• LFCC/MFCC feature extraction

• Spectrogram extraction

• Feature normalization

3. Feature Projection

Some provided tools need to process the features before they can be used for verification. In the SPEAR, this step is referenced as the projection step. Again, the projection might require training, which is executed using the extracted features from the training set. Afterward, all features are projected (using the previously trained Projector).

4. Model Enrollment

Model enrollment defines the stage, where several (projected or unprojected) features of one identity are used to enroll the model for that identity. In the easiest case, the features are simply averaged, and the average feature is used as a model. More complex procedures, which again might require a model enrollment training stage, create models in a different way.

5. Scoring

In the final scoring stage, the models are compared to probe features and a similarity score is computed for each pair of model and probe. Some of the models (the so-called T-Norm-Model) and some of the probe features (so-called Z-Norm-probe-features) are split up, so they can be used to normalize the scores later on.

6. Fusion

The score fusion of different score outputs uses logistic regression.

7. Evaluation

One way to compute the final result is to use the bin/evaluate.py e.g., by calling:

$ bin/evaluate.py -d PATH/TO/USER/DIRECTORY/scores-dev -e PATH/TO/USER/DIRECTORY/scores-eval -c EER -D DET.pdf -x

This will compute the EER, the minCLLR, CLLR, and draw the DET curve.

1. Voxforge dataset

Voxforge is a free database used in free speech recognition engines. We randomly selected a small part of the english corpus (< 1GB). It is used as a toy example for our speaker recognition tool since experiment can be easily run on a local machine, and the results can be obtained in a reasonnable amount of time (< 2h).

Unlike TIMIT and BANCA, this dataset is completely free of charge.

More details about how to download the audio files used in our experiments, and how the data is split into Training, Development and Evaluation set can be found here:

https://pypi-hypernode.com/pypi/xbob.db.voxforge

One example of command line is:

$ ./bin/spkverif_gmm.py -d config/database/voxforge.py -p config/preprocessing/energy.py \
 -f config/features/mfcc_60.py -t config/tools/ubm_gmm/ubm_gmm_256G.py -b ubm_gmm -z \
 --user-directory PATH/TO/USER/DIR --temp-directory PATH/TO/TEMP/DIR

In this example, we used the following configuration:

• Energy-based VAD,

• (19 MFCC features + Energy) + First and second derivatives,

• UBM-GMM Modelling (with 256 Gaussians), the scoring is done using the linear approximation of the LLR.

The performance of the system on DEV and EVAL are:

• DEV: EER = 2.00%

• EVAL: HTER = 1.65%

Another example is to use ISV toolchain instead of UBM-GMM:

$ ./bin/spkverif_isv.py -d config/database/voxforge.py -p config/preprocessing/energy.py \
 -f config/features/mfcc_60.py -t config/tools/isv/isv_256g_u50.py  -z -b isv \
 --user-directory PATH/TO/USER/DIR --temp-directory PATH/TO/TEMP/DIR

• DEV: EER = 1.41%

• EVAL: HTER = 1.56%

One can also try JFA toolchain:

$ ./bin/spkverif_jfa.py -d config/database/voxforge.py -p config/preprocessing/energy.py \
 -f config/features/mfcc_60.py -t config/tools/jfa/jfa_256_v5_u10.py  -z -b jfa \
 --user-directory PATH/TO/USER/DIR --temp-directory PATH/TO/TEMP/DIR

• DEV: EER = 5.65%

• EVAL: HTER = 4.82%

or also IVector toolchain where Whitening, L-Norm, LDA, WCCN are used like in this example where the score computation is done using Cosine distance:

$ ./bin/spkverif_ivector.py -d config/database/voxforge.py -p config/preprocessing/energy.py \
 -f config/features/mfcc_60.py -t config/tools/ivec/ivec_256g_t100_cosine.py -z -b ivector_cosine \
 --user-directory PATH/TO/USER/DIR --temp-directory PATH/TO/TEMP/DIR

• DEV: EER = 15.33%

• EVAL: HTER = 15.78%

The scoring computation can also be done using PLDA:

$ ./bin/spkverif_ivector.py -d config/database/voxforge.py -p config/preprocessing/energy.py \
 -f config/features/mfcc_60.py -t config/tools/ivec/ivec_256g_t100_plda.py -z -b ivector_plda \
 --user-directory PATH/TO/USER/DIR --temp-directory PATH/TO/TEMP/DIR

• DEV: EER = 15.33%

• EVAL: HTER = 16.93%

Note that in the previous examples, our goal is not to optimize the parameters on the DEV set but to provide examples of use.

2. BANCA dataset

BANCA is a simple bimodal database with relatively clean data. The results are already very good with a simple baseline UBM-GMM system. An example of use can be:

$ bin/spkverif_gmm.py -d config/database/banca_audio_G.py -p config/preprocessing/energy.py \
  -f config/features/mfcc_60.py -t config/tools/ubm_gmm/ubm_gmm_256G_regular_scoring.py \
  --user-directory PATH/TO/USER/DIR --temp-directory PATH/TO/TEMP/DIR -z

The configuration in this example is similar to the previous one with the only difference of using the regular LLR instead of its linear approximation.

Here is the performance of this system:

• DEV: EER = 1.66%

• EVAL: EER = 0.69%

3. TIMIT dataset

TIMIT is one of the oldest databases (year 1993) used to evaluate speaker recognition systems. In the following example, the processing is done on the development set, and LFCC features are used:

$ ./bin/spkverif_gmm.py -d config/database/timit.py -p config/preprocessing/energy.py \
  -f config/features/lfcc_60.py -t config/tools/ubm_gmm/ubm_gmm_256G.py \
  --user-directory PATH/TO/USER/DIR --temp-directory PATH/TO/TEMP/DIR -b lfcc -z --groups dev

Here is the performance of the system on the Development set:

• DEV: EER = 2.68%

4. MOBIO dataset

This is a more challenging database. The noise and the short duration of the segments make the task of speaker recognition relatively difficult. The following experiment on male group uses the 4Hz modulation energy based VAD, and the ISV (with dimU=50) modelling technique:

$ ./bin/spkverif_isv.py -d config/database/mobio_male_twothirds_wav.py -p config/preprocessing/mod_4hz.py \
 -f config/features/mfcc_60.py -t config/tools/isv/isv_u50.py \
 --user-directory PATH/TO/USER/DIR --temp-directory PATH/TO/TEMP/DIR -z

Here is the performance of this system:

• DEV: EER = 10.40%

• EVAL: EER = 10.36%

5. NIST SRE 2012

We first invite you to read the paper describing our system submitted to the NIST SRE 2012 Evaluation. The protocols on the development set are the results of a joint work by the I4U group. To reproduce the results, please check this dedicated package:

https://pypi-hypernode.com/pypi/spear.nist_sre12