Sentence examples for estimation of speaker from inspiring English sources

Because the REALDATA were noisier than the SIMDATA, the estimation of speaker vector can be unstable.

To group temporally disjoint time-frequency (T-F) regions, a system [3] employs speaker models to perform a joint estimation of speaker identities and sequential grouping.

In this paper, we investigate the regularized estimation of the speaker-dependent eigenphone matrix for speaker adaptation.

The accuracy of the CASA methods in single channel speech separation depends on the correctness of the pitch frequency estimation of two simultaneous speakers because the proper mask in the T-F domain for the speech separation is produced in association with the estimated pitch frequency.

Experiment 3 builds upon this to further analyze an open question from [28,42], where POLQA and ViSQOL show inconsistent quality estimations for some combinations of speaker and playout adjustments.

It is constructed the same way the dictionary is constructed when using the conventional NMF-based VC, as explained in Section 3. The indexes and weights of the bases are estimated using (4) as source activity H s. Fig. 4 Estimation of parallel dictionary using a speaker transformation matrix.

Estimation of the eigenphones for unknown speakers is not studied.

It can be seen that this expression requires, an estimation of the power of the th speaker, and, an estimation of the decomposed noise covariance matrix.

The estimation of the self-normalization warp,, for speaker, is done in a manner similar to offline VTLN.

Then, the process of language total variability space estimation is exactly the same as the process of total variability space estimation and eigenvoice space estimation in speaker recognition.

In this paper, we investigate various regularization methods to improve the robustness of the estimation of the eigenphone matrix in eigenphone-based speaker adaptation.