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i-vector的工具箱

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MSR Identity Toolbox: A Matlab Toolbox for Speaker Recognition Research Version 1.0 Seyed Omid Sadjadi, Malcolm Slaney, and Larry Heck Microsoft Research, Conversational Systems Research Center (CSRC) s.omid.sadjadi@gmail.com, {mslaney,larry.heck}@microsoft.com This report serves as a user manual for the tools available in the Microsoft Research (MSR) Identity Toolbox. This toolbox contains a collection of Matlab tools and routines that can be used for research and development in speaker recognition. It provides researchers with a test bed for developing new front-end and back-end techniques, allowing replicable evaluation of new advancements. It will also help newcomers in the field by lowering the “barrier to entry”, enabling them to quickly build baseline systems for their experiments. Although the focus of this toolbox is on speaker recognition, it can also be used for other speech related applications such as language, dialect and accent identification. In recent years, the design of robust and effective speaker recognition algorithms has attracted significant research effort from academic and commercial institutions. Speaker recognition has evolved substantially over the past 40 years; from discrete vector quantization (VQ) based systems to adapted Gaussian mixture model (GMM) solutions, and more recently to factor analysis based Eigenvoice (i-vector) frameworks. The Identity Toolbox provides tools that implement both the conventional GMM-UBM and state-of-the-art i-vector based speaker recognition strategies. A speaker recognition system includes two primary components: a front-end and a back-end. The front-end transforms acoustic waveforms into more compact and less redundant representations called acoustic features. Cepstral features are most often used for speaker recognition. It is practical to only retain the high signal-to-noise ratio (SNR) regions of the waveform, therefore there is also a need for a speech activity detector (SAD) in the front-end. After dropping the low SNR frames, acoustic features are further post-processed to remove the linear channel effects. Cepstral mean and variance normalization (CMVN) is commonly used for the post-processing. The CMVN can be applied globally over the entire recording or locally over a sliding window. Feature warping, which is also applied over a sliding window, is another popular feature normalization technique that has been successfully applied for speaker recognition. This toolbox provides support for these normalization techniques, although no tool for feature extraction or SAD is provided. The Auditory Toolbox (Malcolm Slaney) and VOICEBOX (Mike Brooks) which are both written in Matlab can be used for feature extraction and SAD purposes. The main component of every speaker recognition system is the back-end where speakers are modelled (enrolled) and verification trials are scored. The enrollment phase includes estimating a model that represents (summarizes) the acoustic (and often phonetic) space of each speaker. This is usually accomplished with the help of a statistical background model from which the speaker-specific models are adapted. In the conventional GMM-UBM framework the universal background model (UBM) is a Gaussian mixture model (GMM) that is trained on a pool of data (known as the background or development data) from a large number of speakers. The speaker-specific models are then adapted from the UBM using the maximum a posteriori (MAP) estimation. During the evaluation phase, each test segment is scored either against all enrolled speaker models to determine who is speaking (speaker identification), or against the background model and a given speaker model to accept/reject an identity claim (speaker verification). On the other hand, in the i-vector framework the speaker models are estimated through a procedure called Eigenvoice adaptation. A total variability subspace is learned from the development set and is used to estimate a low (and fixed) dimensional latent factor called the identity vector (i-vector) from adapted mean supervectors (the term “i-vector” sometimes also refers to a vector of “intermediate” size, bigger than the underlying cepstral feature vector but much smaller than the GMM supervector). Unlike the GMM-UBM framework, which uses acoustic feature vectors to represent the test segments, in the i-vector paradigm both the model and test segments are represented as i-vectors. The dimensionality of the i-vectors are normally reduced through linear discriminant analysis (with Fisher criterion) to annihilate the non-speaker related directions (e.g., the channel subspace), thereby increasing the discrimination between speaker subspaces. Before modelling the dimensionality reduced i-vectors via a generative factor analysis approach called the probabilistic LDA (PLDA), they are mean and length normalized. In addition, a whitening transformation that is learned from i-vectors in the development set is applied. Finally, a fast and linear strategy, which computes the log-likelihood ratio (LLR) between same versus different speakers hypotheses, scores the verification trials. The Identity toolbox provides tools for speaker recognition using both the GMM-UBM and i-vector paradigms. This report does not provide a detailed description of each speaker recognition tool available. The function descriptions include references to more detailed descriptions of corresponding components. We have attempted to maintain consistency with the naming convention in the code to follow the formulation and symbolization used in the literature. This will make it easier for the users to compare the theory with the implementation and help them better understand the concept behind each algorithm. Usage In order to better support interactive or batch usage, most of the tools in the Identity Toolbox accept either floating point or string arguments. String arguments, either for a file name or a numerical value, are useful when these tools are compiled and called from a shell command line. This makes it easy to use the tools on machines with limited memory (but enough disk space) as well as computer clusters (from a terminal). In addition, the interactive tools can optionally write the output products (models or matrices) to the disk if an output file name is specified. This toolbox makes extensive use of parfor loops (as opposed to for loops) so that parallel processing can speed up the computations. However, if the Distributed Computing Toolbox is not installed, Matlab automatically considers all parfor loops as for loops and there is no need to modify the tools. Matlab by default sets the number of parallel workers to the number of physical CPU cores (not logical threads!) available on a computer. At the time of writing this report, Matlab supports a maximum of 12 workers on a local machine. The Identity toolbox has been tested on Windows 8 as well as Ubuntu Linux computers running Matlab R2013a. The toolbox is portable and is expected to work on any machine that runs Matlab. Compilation In case Matlab is not installed or Matlab license is not available (for instance on a computer cluster), we provide standalone executables that can be used in conjunction with the Matlab Compiler Runtime (MCR). The MCR is a standalone set of shared libraries that enables the execution of compiled Matlab applications or components on computers that do not have Matlab installed. The MCR installer can be obtained free of charge from the web address: http://www.mathworks.com/products/compiler/mcr/ The binaries supplied with this version of the toolkit need version 8.1 (R2013a) of the MCR. The MCR installer is easy to use and provides users with an installation wizard. Assuming that the MCR is installed, a Matlab code can be compiled from either the command window or a DOS/bash terminal as: mcc -m -R -singleCompThread -R -nodisplay -R -nojvm foo.m -I libs/ -o foo -d bin/ for a standalone single-threaded executable. Single-threaded executables are useful when running the tools on clusters that only allow a single CPU process per scheduled job. To generate multithreaded executables (this is important when using parfor) the mcc can be used as following: mcc -m -R -nodisplay foo.m -I libs/ -o foo -d bin/ For more details on the “mcc” command see the Matlab documentation. Flow Charts The Identity toolbox provides researchers with tools that implement both the conventional GMM-UBM and state-of-the-art i-vector based systems. The block diagrams below show the overall signal flow and the routines (page numbers in parenthesis) used by each system. GMM-UBM i-vector-PLDA cmvn Purpose Global cepstral mean and variance normalization (CMVN) Synopsis Fea = cmvn(fea, varnorm) Description This function implements global cepstral mean and variance normalization (CMVN) on input feature matrix fea to remove the linear channel effects. The code assumes that there is one observation per column. The CMVN should be applied after dropping the low SNR frames. The logical switch varnorm (false | true) is used to instruct the code to perform variance normalization in addition to mean normalization. Examples In an example we plot the distribution (histogram) of (first cepstral coefficient) in sample feature file, before and after global CMVN: >> load('mfcc') >> size(mfcc) ans = 39 24252 >> hist(mfcc(2,:), 30) >> hist(cmvn(mfc(2,:), true), 30) As expected there is no change in overall shape of the distribution, and only the dynamic range of the feature stream is modified. wcmvn Purpose Cepstral mean and variance normalization (CMVN) over a sliding window Synopsis Fea = wcmvn(fea, win, varnorm) Description This function implements cepstral mean and variance normalization (CMVN) on input feature matrix fea to remove the (locally) linear channel effects. The code assumes that there is one observation per column. The normalization is performed over a sliding window that typically spans 301 frames (that is 3 seconds at a typical 100 Hz frame rate). The middle frame in the window is normalized based on the mean and variance computed over the specified time interval. The length of the sliding window can be specified through the scalar input win which must be an odd number. The CMVN should be applied after dropping the low SNR frames. The logical scalar varnorm (false | true) is used to instruct the code to perform variance normalization in addition to mean normalization. The normalized feature streams are return in Fea. Examples In this example we plot the distribution (histogram) of (first cepstral coefficient) in a sample feature file, before and after windowed CMVN: >> load('mfcc') >> size(mfcc) ans = 39 24252 >> hist(mfcc(2,:), 30) >> hist(wcmvn(mfc(2,:), 301, true), 30) Unlike with the global CMVN, for this sample feature stream the overall shape of the feature stream distribution is approximately mapped to a standard normal distribution. fea_warping Purpose Short-term Gaussianization over a sliding window (a.k.a feature warping) Synopsis Fea = fea_warping(fea, win) Description This routine warps the distribution of the cepstral feature streams in fea to the standard normal distribution (i.e., ) to mitigate the effects of (locally) linear channel mismatch. This is specifically useful because the distribution of cepstral feature streams is often modeled by Gaussians. The code assumes that there is one observation per column. The normalization is performed over a sliding window that typically spans 301 frames (that is 3 seconds at a typical 100 Hz frame rate). The middle frame in the window is normalized based on its rank in a array of sorted feature values over the specified time interval. The length of the sliding window is specified through the scalar input win which must be an odd number. Fea contains the normalized feature streams. Note that the feature warping should be applied after dropping the low SNR frames. Examples In this example we plot the distribution (histogram) of (first cepstral coefficient) in a sample feature file, before and after feature warping: >> load('mfcc') >> size(mfcc) ans = 39 24252 >> hist(mfcc(2,:), 30) >> hist(fea_warping(mfc(2,:), 301), 30) Notice that the overall distribution of the feature stream is warped to the standard normal distribution. See Also [1] J. Pelecanos and S. Sridharan, “Feature warping for robust speaker veriﬁcation,” in Proc. ISCA Odyssey, Crete, Greece, Jun. 2001. gmm_em Purpose Fit a Gaussian mixture model (GMM) to observations Synopsis gmm = gmm_em(dataList, nmix, final_niter, ds_factor, nworkers, gmmFilename) Description This function fits a GMM to acoustic feature vectors using binary splitting and expectation-maximization (EM). The input argument dataList can be either the name of an ASCII list containing feature file names (assuming one file per line), or a cell array containing features (assuming one feature matrix per cell). In case a list of files (the former option) is provided, the features must be saved in uncompressed HTK format. In case a cell array of features is provided, the function assumes one observation per column. The scalar nmix specifies the number of desired components in the GMM, and must be a power of 2. A binary splitting procedure is used to boot up the GMM from a single component to nmix components. After each split the model is re-estimated several times using the EM algorithm. The number of EM iterations at each split is gradually increased from 1 to final_niter (scalar) for the nmix component GMM. While booting up a GMM (from one to nmix components) on a large number of observations, it is practical to down-sample (sub-sample) the acoustic features. It is usually not necessary to re-estimate the model parameters at each split using all feature frames. This is due to the redundancy of speech frames and the fact that the analysis frames are overlapping. The scalar argument ds_factor specifies the down-sampling factor. The value assigned to the ds_factor is reset to one in the last two splits. The scalar argument nworkers specifies the number of MATLAB parallel workers in the parfor loop. MATLAB by default sets the number of workers to the number of Cores (not virtual processors!) available on a computer. At the time of writing this report, MATLAB only supports a maximum of 12 workers on a local machine. The optional argument gmmFilename (string) specifies the file name of GMM model to be saved. If this is specified, the GMM hyper-parameters (as structure fields, see below) are saved in a .mat file on disk. The model hyper-parameters are returned in gmm which is a structure with three fields: gmm.mu component means gmm.sigma component covariance matrices gmm.w component weights The code reports the accumulated likelihood of observations given the model in each EM iteration. It also reports the elapsed time for each iteration. mapAdapt Purpose Adapt a speaker specific GMM from a universal background model (UBM) Synopsis gmm = mapAdapt(dataList, ubm, tau, config, gmmFilename) Description This routine adapts a speaker specific GMM from a UBM using maximum a posteriori (MAP) estimation. The adaptation data is specified input via dataList, which should be either the name of an ASCII list containing feature file names (assuming one file per line), or a cell array containing features (assuming one feature matrix per cell). In case a list of files is provided, the features must be saved in uncompressed HTK format. The input argument ubm can be either a file name (string) or a structure with UBM hyper-parameters (in form of gmm.mu, gmm.sigma, and gmm.w, see also gmm_em). The UBM file should be a .mat file with the same structure as above. The code supports adaptation of all model hyper-parameters (i.e., means, covariance matrices, and weights). The input string parameter config is used to specify which parameters should be adapted. Any sensible combination of ‘m’, ‘v’, and ‘w’ is accepted (default is mean ‘m’). The MAP adaptation relevance factor is set via the scalar input tau. The optional argument gmmFilename (string) specifies the file name of the adapted GMM model to be saved. If this is specified, the GMM hyper-parameters (as structure fields, see below) are saved in a .mat file on disk. The model hyper-parameters are returned in gmm, which is a structure with three fields (i.e., gmm.mu, gmm.sigma, gmm.w). See Also [1] D.A. Reynolds, T.F. Quatieri, R.B. Dunn, “Speaker verification using adapted Gaussian mixture models”, Digital Signal Processing, vol. 10, pp. 19-41, Jan. 2000. score_gmm_trials Purpose Compute verification scores for GMM trials Synopsis scores = score_gmm_trials(models, tests, trials, ubmFilename) Description This function computes the verification scores for trials specified in the input argument trials. The scores are computed as the log-likelihood ratio between the given speaker models and the UBM given the test observations. The input argument models is a cell array containing the speaker models. The speaker models are GMM structures with fields described before (see also gmm_em). The input argument tests is also cell array that should either contain the feature matrices or the feature file names. The input argument trials is a 2-dimensional array with 2 columns. The first column contains the numerical model IDs (1 ... N, assuming N models), while the second column contains the numerical test IDs (1 … M, assuming M test files). Each row of the two-column array specifies a model-test trial (e.g., [3 10] means model number 3 should be tested against test segment 10). The input argument ubmFilename can be either a file name (string) or a structure with UBM hyper-parameters (in form of gmm.mu, gmm.sigma, and gmm.w, see also gmm_em). The UBM file should be a .mat file with the same structure as above. The verification likelihood ratios are returned in scores (one score per trial). See Also [1] D.A. Reynolds, T.F. Quatieri, R.B. Dunn, “Speaker verification using adapted Gaussian mixture models,” Digital Signal Processing, vol. 10, pp. 19-41, Jan. 2000. compute_bw_stats Purpose Compute the sufficient statistics for observations given the UBM Synopsis [N, F] = compute_bw_stats(fea, ubm, statFilename) Description This function computes the zeroth (N) and first (F) order sufficient statistics (Baum-Welch statistics) for observations given a UBM: where denotes the posterior probability of the UBM mixture component given the observations . The input argument fea can be either a feature file name (string) or a feature matrix with one observation per column. In case a file name is provided, the features must be saved in uncompressed HTK format. The input argument ubm can be either a file name (string) or a structure with UBM hyper-parameters (in form of gmm.mu, gmm.sigma, and gmm.w, see also gmm_em). The UBM file should be a .mat file with the same structure as above. The optional argument statFilename (string) specifies the stat file name to be saved. If this is specified, the statistics are saved in a .mat file on disk. The zeroth order statistic, N, is a one-dimensional array with nmix elements (i.e., the number of Gaussian components from the UBM). The first order statistic, F, is also a one-dimensional array with nmix × ndim components (i.e., the supervector dimension). The first order statistic is centered. See Also [1] N. Dehak, P. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification,” IEEE TASLP, vol. 19, pp. 788-798, May 2011. [2] P. Kenny, "A small footprint i-vector extractor," in Proc. ISCA Odyssey, The Speaker and Language Recognition Workshop, Singapore, Jun. 2012. train_tv_space Purpose Learn a total variability subspace from the observations Synopsis T = train_tv_space(dataList, ubm, tv_dim, niter, nworkers, tvFilename) Description This routine uses EM to learn a total variability subspace from the observations. Technically, assuming a factor analysis (FA) model of the form: for mean supervectors, , the code computes the maximum likelihood estimate (MLE) of the factor loading matrix (a.k.a. the total variability subspace). Here, is the adapted mean supervector, is the UBM mean supervector, and is a vector of total factors (a.k.a. the i-vector). The observations are assumed to be in form of sufficient statistics computed with the background model (UBM). The input argument dataList is either the name (string) of an ASCII list containing statistics file names (one file per line), or a cell array of concatenated stats that is the zeroth order stats, N, appended with the first order stats, F, in a column vector. The input argument ubm can be either a file name (string) or a structure with UBM hyper-parameters (in form of gmm.mu, gmm.sigma, and gmm.w, see also gmm_em). The UBM file should be a .mat file with the same structure as described above. The scalar input tv_dim specifies the dimensionality of the total subspace. The tv_dim values typically range from 400 to 800. The total subspace is learned in an EM framework. The number of EM iterations can be set using the scalar niter argument. The accumulation of statistics in each EM iteration can be sped up using a parfor loop. The scalar argument nworkers specifies the number of MATLAB parallel workers in the parfor loop. The optional argument tvFilename (string) specifies the output file name. If this is specified, the total subspace matrix is saved in a .mat file on disk. See Also [1] D. Matrouf, N. Scheffer, B. Fauve, J.-F. Bonastre, “A straightforward and efficient implementation of the factor analysis model for speaker verification,” in Proc. INTERSPEECH, Antwerp, Belgium, Aug. 2007, pp. 1242-1245. [2] N. Dehak, P. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification,” IEEE TASLP, vol. 19, pp. 788-798, May 2011. [3] P. Kenny, “A small footprint i-vector extractor,” in Proc. ISCA Odyssey, The Speaker and Language Recognition Workshop, Singapore, Jun. 2012. [4] “Joint Factor Analysis Matlab Demo,” 2008. [Online]. Available: http://speech.fit.vutbr.cz/software/joint-factor-analysis-matlab-demo/. extract_ivector Purpose Compute the identity vector (i-vector) for observations Synopsis x = extract_ivector(stat, ubm, tv_matrix, ivFilename) Description This function computes the i-vector for observations as the mean (conditional expectation) of the posterior distribution of the latent variable. The observations are assumed to be in form of sufficient statistics computed with the background model (UBM). The input argument stat is either the name (string) of .mat file containing the statistics or a one-dimensional array of concatenated stats, that is the zeroth order stats, N, appended with the first order stats, F, in a column vector. The input argument ubm can be either a file name (string) or a structure with UBM hyper-parameters (specifying gmm.mu, gmm.sigma, and gmm.w, see also gmm_em). The UBM file should be a .mat file with this same structure. The i-vector extractor tv_matrix can be specified either with a file name (string) or a matrix. The code can optionally save the i-vectors into a .mat file. The input argument ivFilename specifies the output file name. The i-vector is returned in a column vector of size tv_dim (see also train_tv_space). See Also [1] D. Matrouf, N. Scheffer, B. Fauve, J.-F. Bonastre, “A straightforward and efficient implementation of the factor analysis model for speaker verification,” in Proc. INTERSPEECH, Antwerp, Belgium, Aug. 2007, pp. 1242-1245. [2] P. Kenny, “A small footprint i-vector extractor,” in Proc. ISCA Odyssey, The Speaker and Language Recognition Workshop, Singapore, Jun. 2012. [3] N. Dehak, P. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification,” IEEE TASLP, vol. 19, pp. 788-798, May 2011. lda Purpose Linear discriminant analysis (LDA) using Fisher criterion Synopsis [V, D] = lda(data, labels) Description This routine computes a linear transformation that maximizes the between class variation while minimizing the within class variances. It uses the Fisher criterion for this purpose. Technically, the Fisher criterion to be maximized is in the form: where and are between- and within-class covariance matrices, respectively. The above relationship is a Rayleigh quotient, therefore the solution, , is the generalized eigenvectors of . The input argument data is a two-dimensional array that specifies the data matrix, assuming one observation per column. Class labels for observations in the data matrix can be specified via labels which is a one dimensional array (or cell array) with one numerical (or string) element per class. The LDA transformation matrix (generalized eigenvectors stored in columns) is returned in V. Note that the maximum number of columns in V is the minimum of dimensionality of observations and the number of unique class minus 1. The generalized eigenvalues are returned in D. See Also [1] K. Fukunaga, Introduction to Statistical Pattern Recognition. 2nd ed. New York: Academic Press, 1990, ch. 10. gplda_em Purpose Learn a Gaussian probabilistic LDA (PLDA) from observations Synopsis plda = gplda_em(data, spk_labs, nphi, niter) Description This function uses EM to learn a Gaussian PLDA model from observations. The observations are i-vectors computed from the development set. The input argument data contains the i-vectors (one observation per column). The development i-vectors are internally centered (mean is removed), length-normalized, and whitened before modeling. Technically, assuming a factor analysis (FA) model of the i-vectors of the form: , this routine computes the maximum likelihood estimate (MLE) of the factor loading matrix (a.k.a. the Eigenvoice subspace). Here, is the i-vector, is the mean of training i-vectors, and is a vector of latent factors. The full covariance residual noise term explains the variability not captured through the latent variables. The input argument spk_labs determines the class (i.e., speaker) labels for observations in the data matrix. spk_labs is a one-dimensional array (or cell array) with one numerical (or string) element per class. The dimensionality of the Eigenvoice subspace is specified using scalar argument nphi. The scalar input niter determines the number of EM iteration for learning the PLDA model. The Gaussian PLDA model is returned in plda, which is a structure with fields: plda.Phi Eigenvoice matrix plda.Sigma covariance matrix of the residual noise (full) plda.M mean of the development i-vectors plda.W whitening transformation See Also [1] S.J.D. Prince and J.H. Elder, “Probabilistic linear discriminant analysis for inferences about identity,” in Proc. IEEE ICCV, Rio de Janeiro, Brazil, Oct. 2007. [2] D. Garcia-Romero and C.Y. Espy-Wilson, “Analysis of i-vector length normalization in speaker recognition systems,” in Proc. INTERSPEECH, Florence, Italy, Aug. 2011, pp. 249-252. [3] P. Kenny, “Bayesian speaker verification with heavy-tailed priors,” in Proc. Odyssey, The Speaker and Language Recognition Workshop, Brno, Czech Republic, Jun. 2010. score_gplda_trials Purpose Compute verification scores for i-vector trials using the PLDA model Synopsis scores = score_gplda_trials(plda, model_iv, test_iv) Description This function computes the verification scores for all possible model-test i-vector trials. The scores are computed as the “batch” log-likelihood ratio between the same () versus different () speaker models hypotheses: The i-vectors, , are modeled with a Gaussian PLDA provided via plda. The input plda model is a structure with PLDA hyperparameters (i.e., plda.Phi, plda.Sigma, plda.M, and plda.W). Before computing the verification scores, the enrollment and test i-vectors are internally mean- and length-normalized and whitened. The input arguments model_iv and test_iv are two-dimensional arrays (one observation per column) containing unprocessed enrollment and test i-vectors, respectively. The likelihood ratio test has a linear and closed form solution. Therefore, it is practical to compute the verification scores at once for all possible combination of model-test i-vectors, and then select a subset of scores according to a trial list. The output argument scores is a matrix that contains the verification scores for all possible trials. See Also [1] D. Garcia-Romero and C.Y. Espy-Wilson, “Analysis of i-vector length normalization in speaker recognition systems,” in Proc. INTERSPEECH, Florence, Italy, Aug. 2011, pp. 249-252. [2] P. Kenny, “Bayesian speaker verification with heavy-tailed priors,” in Proc. Odyssey, The Speaker and Language Recognition Workshop, Brno, Czech Republic, Jun. 2010. compute_eer Purpose Compute the equal error rate (EER) performance measure Synopsis [eer, dcf08, dcf10] = compute_eer(scores, labels, showfig) Description This routine computes the EER given the verification scores for target and impostor trials. The EER is calculated as the operating point on the detection error tradeoff (DET) curve where the false-alarm and missed-detection rates are equal. The input argument scores is a one-dimensional array containing the verification scores for all target and impostor trials. The trial labels are specified via the argument labels which can be a one-dimensional binary array (0’s and 1’s for impostor and target), or a cell array with “target” and “impostor” string labels. The logical switch showfig (false | true) is used to instruct the code as to whether the DET curve should be plotted. The EER is returned in eer (in percent). Additionally, the minimum detection cost functions (DCF) are computed and returned if the optional output arguments dcf08 and dcf10 are specified. The dcf08 (×100) is computed according to the NIST SRE 2008 cost parameters, while the dcf10 (×100) is calculated based on the NIST SRE 2010 parameters. See Also [1] “The NIST year 2008 speaker recognition evaluation plan,” 2008. [Online]. Available: http://www.nist.gov/speech/tests/sre/2008/sre08_evalplan_release4.pdf [2] “The NIST year 2010 speaker recognition evaluation plan,” 2010. [Online]. Available: http://www.itl.nist.gov/iad/mig/tests/sre/2010/NIST_SRE10_evalplan.r6.pdf Demos Introduction We demonstrate the use of this toolbox with two different kinds of demonstrations. The first example demonstrates that this toolbox can achieve state-of-the-art performance on a standard identity task, using the TIMIT corpus. The second demonstration uses artificial data to show the simplest usage cases for the toolbox. TIMIT Task In order to demonstrate how the tools in the Identity Toolbox work individually and when combined together, we provide two sample demos using the TIMIT corpus: 1) demo_gmm_ubm and 2) demo_ivector_plda. The first and the second demo show how to use the tools to run speaker recognition experiments in a GMM-UBM and i-vector frameworks, respectively. A relatively small scale speaker verification task has been designed using speech material from the TIMIT corpus. There are a total of 630 (192 female and 438 male) speakers in TIMIT, from which 530 speakers have been selected for background model training and the remaining 100 (30 female and 70 male) speakers are used for tests. There are 10 short sentences per speaker in TIMIT. For background model training all sentences from all 530 speakers (i.e., 5300 speech recordings in total) are used. For speaker-specific model training 9 out of 10 sentences per speaker are selected and the remaining 1 sentence is kept for tests. Verification trials consist of all possible model-test combinations, resulting in a total of 10,000 trials (100 target versus 9900 impostor trials). The figure below shows the detection error tradeoff (DET) curves for the two systems: GMM-UBM (solid) and i-vector-PLDA (dashed). Also shown in the figure are the system performances on the TIMIT task in terms of the EER. The EER operating points are circled as the intersection of a diagonal line with the DET curves. Demos Artificial Task A small-scale task generates artificial features for 20 speakers. Each speaker has 10 sessions (channels) and each session is 1000 frames long (which translates to 10 seconds assuming a frame rate of 100 Hz). The following script (demo_create_data.m) generates the features used in the following demonstrations: nSpeakers = 20; nDims = 13; % dimensionality of feature vectors nMixtures = 32; % How many mixtures used to generate data nChannels = 10; % Number of channels (sessions) per speaker nFrames = 1000; % Frames per speaker (10 seconds assuming 100 Hz) nWorkers = 1; % Number of parfor workers, if available rng('default'); % To promote reproducibility. % Pick random centers for all the mixtures. mixtureVariance = .10; channelVariance = .05; mixtureCenters = randn(nDims, nMixtures, nSpeakers); channelCenters = randn(nDims, nMixtures, nSpeakers, nChannels)*.1; trainSpeakerData = cell(nSpeakers, nChannels); testSpeakerData = cell(nSpeakers, nChannels); speakerID = zeros(nSpeakers, nChannels); % Create the random data. Both training and testing data have the same % layout. for s=1:nSpeakers trainSpeechData = zeros(nDims, nMixtures); testSpeechData = zeros(nDims, nMixtures); for c=1:nChannels for m=1:nMixtures % Create data from mixture m for speaker s frameIndices = m:nMixtures:nFrames; nMixFrames = length(frameIndices); trainSpeechData(:,frameIndices) = ... randn(nDims, nMixFrames)*sqrt(mixtureVariance) + ... repmat(mixtureCenters(:,m,s),1,nMixFrames) + ... repmat(channelCenters(:,m,s,c),1,nMixFrames); testSpeechData(:,frameIndices) = ... randn(nDims, nMixFrames)*sqrt(mixtureVariance) + ... repmat(mixtureCenters(:,m,s),1,nMixFrames) + ... repmat(channelCenters(:,m,s,c),1,nMixFrames); end trainSpeakerData{s, c} = trainSpeechData; testSpeakerData{s, c} = testSpeechData; speakerID(s,c) = s; % Keep track of who this is end end After generating the features are generated we can use them to train and test GMM-UBM and i-vector speaker recognition systems. GMM-UBM Demo There are four steps involved in training and testing a GMM-UBM speaker recognition system: Training a UBM from the background data MAP adapting speaker models from the UBM using enrollment data Scoring verification trials Computing the performance measures (e.g., confusion matrix and EER) The following Matlab script (demo_gmm_ubm_artificial.m) generates a UBM speaker-recognition model and tests it: %% rng('default') % Step1: Create the universal background model from all the % training speaker data nmix = nMixtures; % In this case, we know the # of mixtures needed final_niter = 10; ds_factor = 1; ubm = gmm_em(trainSpeakerData(:), nmix, final_niter, ds_factor, ... nWorkers); %% % Step2: Now adapt the UBM to each speaker to create GMM speaker model. map_tau = 10.0; config = 'mwv'; gmm = cell(nSpeakers, 1); for s=1:nSpeakers gmm{s} = mapAdapt(trainSpeakerData(s, :), ubm, map_tau, config); end %% % Step3: Now calculate the score for each model versus each speaker's % data. % Generate a list that tests each model (first column) against all the % testSpeakerData. trials = zeros(nSpeakers*nChannels*nSpeakers, 2); answers = zeros(nSpeakers*nChannels*nSpeakers, 1); for ix = 1 : nSpeakers, b = (ix-1)*nSpeakers*nChannels + 1; e = b + nSpeakers*nChannels - 1; trials(b:e, :) = [ix * ones(nSpeakers*nChannels, 1), ... (1:nSpeakers*nChannels)']; answers((ix-1)*nChannels+b : (ix-1)*nChannels+b+nChannels-1) = 1; end gmmScores = score_gmm_trials(gmm, reshape(testSpeakerData', ... nSpeakers*nChannels,1), trials, ubm); %% % Step4: Now compute the EER and plot the DET curve and confusion matrix imagesc(reshape(gmmScores,nSpeakers*nChannels, nSpeakers)) title('Speaker Verification Likelihood (GMM Model)'); ylabel('Test # (Channel x Speaker)'); xlabel('Model #'); colorbar; drawnow; axis xy figure eer = compute_eer(gmmScores, answers, false); This generates the confusion matrix (image) shown below. (The EER curve is blank because recognition is perfect at these noise levels.) i-vector Demo There are five steps involved in training and testing an i-vector speaker recognition system: Training a UBM from the background data Learning a total variability subspace from background statistics Training a Gaussian PLDA model with development i-vectors Scoring verification trials with model and test i-vectors Computing the performance measures (e.g., EER and confusion matrix) The following Matlab script (demo_ivector_plda_artificial.m) demonstrates the use of the i-vector code and shows simple results: %% rng('default'); % Step1: Create the universal background model from all the % training speaker data nmix = nMixtures;% In this case, we know the # of mixtures needed final_niter = 10; ds_factor = 1; ubm = gmm_em(trainSpeakerData(:), nmix, final_niter, ... ds_factor, nWorkers); %% % Step2.1: Calculate the statistics needed for the iVector model. stats = cell(nSpeakers, nChannels); for s=1:nSpeakers for c=1:nChannels [N,F] = compute_bw_stats(trainSpeakerData{s,c}, ubm); stats{s,c} = [N; F]; end end % Step2.2: Learn the total variability subspace from all the speaker data. tvDim = 100; niter = 5; T = train_tv_space(stats(:), ubm, tvDim, niter, nWorkers); % % Now compute the ivectors for each speaker and channel. % The result is size % tvDim x nSpeakers x nChannels devIVs = zeros(tvDim, nSpeakers, nChannels); for s=1:nSpeakers for c=1:nChannels devIVs(:, s, c) = extract_ivector(stats{s, c}, ubm, T); end end %% % Step3.1: Now do LDA on the iVectors to find the dimensions that % matter. ldaDim = min(100, nSpeakers-1); devIVbySpeaker = reshape(devIVs, tvDim, nSpeakers*nChannels); [V,D] = lda(devIVbySpeaker, speakerID(:)); finalDevIVs = V(:, 1:ldaDim)' * devIVbySpeaker; % Step3.2: Now train a Gaussian PLDA model with development % i-vectors nphi = ldaDim; % should be <= ldaDim niter = 10; pLDA = gplda_em(finalDevIVs, speakerID(:), nphi, niter); %% % Step4.1: OK now we have the channel and LDA models. Let's build % actual speaker % models. Normally we do that with new enrollment data, but now % we'll just reuse the development set. averageIVs = mean(devIVs, 3); % Average IVs across channels. modelIVs = V(:, 1:ldaDim)' * averageIVs; % Step4.2: Now compute the ivectors for the test set % and score the utterances against the models testIVs = zeros(tvDim, nSpeakers, nChannels); for s=1:nSpeakers for c=1:nChannels [N, F] = compute_bw_stats(testSpeakerData{s, c}, ubm); testIVs(:, s, c) = extract_ivector([N; F], ubm, T); end end testIVbySpeaker = reshape(permute(testIVs, [1 3 2]), ... tvDim, nSpeakers*nChannels); finalTestIVs = V(:, 1:ldaDim)' * testIVbySpeaker; %% % Step5: Now score the models with all the test data. ivScores = score_gplda_trials(pLDA, modelIVs, finalTestIVs); imagesc(ivScores) title('Speaker Verification Likelihood (iVector Model)'); xlabel('Test # (Channel x Speaker)'); ylabel('Model #'); colorbar; axis xy; drawnow; answers = zeros(nSpeakers*nChannels*nSpeakers, 1); for ix = 1 : nSpeakers, b = (ix-1)*nSpeakers*nChannels + 1; answers((ix-1)*nChannels+b : (ix-1)*nChannels+b+nChannels-1) = 1; end ivScores = reshape(ivScores', nSpeakers*nChannels* nSpeakers, 1); figure; eer = compute_eer(ivScores, answers, false); This generates the confusion matrix (image) shown below:

非执行型测试（Non-execution-based testing）是指在软件开发过程中，通过分析和检查设计文档、源代码等软件产品的非运行阶段进行的测试方法。与传统的执行型测试不同，非执行型测试不需要实际运行测试用例，而是通过对软件产品的静态分析和检查来评估软件的质量和可靠性。非执行型测试可以通过多种技术手段进行，比如代码审查、静态分析、模型检测等。代码审查是一种通过仔细阅读和分析源代码，找出潜在问题和错误的方法。静态分析是一种通过分析代码结构、规则和约束条件，发现代码中可能存在的问题和异常情况的方法。模型检测是一种通过建立系统的形式化模型，并使用自动化的验证工具来检测模型中是否存在错误和违反性质的方法。非执行型测试在软件开发过程中具有重要的作用。它可以帮助发现和修复软件产品中的潜在问题和错误，减少在运行阶段发现错误的成本和风险。非执行型测试还可以提高软件的质量和可靠性，提升软件开发过程的效率和可控性。总之，非执行型测试是一种通过分析和检查软件产品的非运行阶段，来评估软件质量和可靠性的方法。它具有重要的作用，可以帮助提高软件开发过程中的效率和可控性，减少成本和风险。

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