I don't think you can reuse the verify method for your purpose, but here's generally the steps you need to be taking:
To perform speaker verification, you need a ground truth speaker embedding. It can be an i-vector, an x-vector, etc. If you've already trained the x-vector model using the recipe in the example, you'll want to perform preprocessing and prediction using the same pipeline. Speaker Diarization Using x-vectors uses the x-vector model and walks through the preprocessing steps. Here is just a sketch of what it would look like:
x = knownspeechsignal;
features = (extract(afe,x)-globalPrecomputedMean)./globalPrecomputedSTD;
embeddingTemplate = predict(model,dlarray(features,'TCB'),Outputs="fc_1");
When you have unkown speech, you perform the same steps.
x = unknownspeechsignal;
features = (extract(afe,x)-globalPrecomputedMean)./globalPrecomputedSTD;
embeddingUnknown = predict(model,dlarray(features,'TCB'),Outputs="fc_1");
To perform speaker verification, you score the two features using either PLDA or CSS. Here's an example of CSS:
css = dot(embeddingTemplate,embeddingUnknown)/norm(embeddingTemplate)*norm(embeddingUnknown);
speakerisverified = css < threshold
You'll need to maintain a list of template embeddings to look up when attempting to perform speaker verification.
Here's a sketch of it all together.
% Create templates for known speakers
x = knownspeechsignal_1;
features = (extract(afe,x)-globalPrecomputedMean)./globalPrecomputedSTD;
embeddingTemplate_1 = predict(model,dlarray(features,'TCB'),Outputs="fc_1");
x = knownspeechsignal_2;
features = (extract(afe,x)-globalPrecomputedMean)./globalPrecomputedSTD;
embeddingTemplate_2 = predict(model,dlarray(features,'TCB'),Outputs="fc_1");
% Create an enrollment list
enrolledSpeakers = dictionary(["speaker 1","speaker 2"],[embeddingTemplate_1,embeddingTemplate_1]);
% Extract embedding from unknown speaker
x = unknownspeechsignal;
features = (extract(afe,x)-globalPrecomputedMean)./globalPrecomputedSTD;
embeddingUnknown = predict(model,dlarray(features,'TCB'),Outputs="fc_1");
% Unknown speaker purports to be speaker 1, verify that:
claimedidentity = "speaker 1";
embeddingTemplate = enrolledSpeakers("speaker 1");
css = dot(embeddingTemplate,embeddingUnknown)/norm(embeddingTemplate)*norm(embeddingUnknown);
speakerisverified = css < threshold
The PLDA model is not currently offered standalone, you can use the internal version that ivectorSystem has at your own risk (it is not intended to be user-facing and may change at any time). To see an example of using it, step through either the x-vector training example or diarization example. Alternatively, this example walks through the nitty-gritty of the entire i-vector system including the G-PLDA scoring: Speaker Verification Using i-vectors.
Also, depending on the difficulty of your speaker verification task, you might consider using the speakerRecognition function to return a pretrained i-vector system.
Please ask for any clarifying questions--I'm hoping to add some examples where the whole detection error tradeoff, identification, verification, are componentized.
