Sentence examples for speaker characteristics from inspiring English sources

The BF-DNN classifies speaker characteristics in the right direction.

Second, speaker characteristics are used to group the T F units across time frames.

There are various approaches for adapting a DNN-HMM to changing acoustic environments or speaker characteristics.

We assume that the speaker characteristics suffer similar distortion in the training data and test data.

However, we believe that parameters with small variances can also reflect speaker characteristics.

The DAE retains speaker characteristics and suppresses the reverberation by nonlinear feature mapping.

The second approach relies on the effective modeling of speaker characteristic in the classifier design (e.g., GMM-UBM, GMM-SVM, JFA, i-vector, PLDA) [4],[7]-[15].

As mentioned earlier, we believe that speaker characteristic is not mainly concentrated on the three dimensions which are related to log energy.

In this framework, it is possible to exploit the data from different languages to predict speaker-specific characteristics of the target speaker, and consequently, the data sparsity problem will be alleviated.

Watermarking of specific spectral regions that are not dependent on the speaker voice characteristics is not in direct conflict with speaker biometric recognition processes and is thus a valid approach for speaker authentication.

For this purpose, a priori categorization of speaker's characteristics using hierarchical methods might be used to simplify the statistical models behind to automatically assess the quality of speech.