Difference between i-vector and d-vector

Question

could someone please explain the difference between i-vector and d-vector? All I know about them is that they are widely used in speaker/speech recognition systems and they are kind of templates for representing speaker information, but I don't know the main differences.

Answer 1

I-vector is a feature that represents the idiosyncratic characteristics of the frame-level features' distributive pattern. I-vector extraction is essentially a dimensionality reduction of the GMM supervector (although the GMM supervector is not extracted when computing the i-vector). It's extracted in a similar manner with the eigenvoice adaptation scheme or the JFA technique, but is extracted per sentence (or input speech sample).

On the other hand, d-vector is extracted using DNN. To extract a d-vector, a DNN model that takes stacked filterbank features (similar to the DNN acoustic model used in ASR) and generates the one-hot speaker label (or the speaker probability) on the output is trained. D-vector is the averaged activation from the last hidden layer of this DNN. So unlike the i-vector framework, this doesn't have any assumptions about the feature's distribution (the i-vector framework assumes that the i-vector, or the latent variable has a Gaussian distribution).

So in conclusion, these are two distinct features extracted from totally different methods or assumptions. I recommend you reading these papers:

N. Dehak, P. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, "Front-end factor analysis for speaker verification," IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, no. 4, pp. 788-798, 2011.

E. Variani, X. Lei, E. McDermott, I. L. Moreno, and J. G-Dominguez, "Deep neural networks for small footprint text-dependent speaker verification," in Proc. ICASSP, 2014, pp. 4080-4084.

Answer 2

I don't know how to properly characterize the d-vector in plain language, but I can help a little.

The identity vector, or i-vector, Is a spectral signature for a particular slice of speech, usually a sliver of a phoneme, rarely (as far as I can see) as large as the entire phoneme. Basically, it's a discrete spectrogram expressed in a form isomorphic to the Gaussian mixture of the time slice.

EDIT

Thanks to those who provided comments and a superior answer. I updated this only to replace the incorrect information from my original attempt.

A d-vector is extracted from a Deep NN, the mean of the feature vectors in the DNN's final hidden layer. This becomes the model for the speaker, used to compare against other speech samples for identification.