Sentence examples for training speaker from inspiring English sources

Good generalization properties of this classifier enahle to achieve low error rates even for short training speaker utterances.

Here, μs;r denotes the mean vector corresponding to mixture r of the s th training speaker model.

In this paper, we address this problem by training speaker models using dereverberant speech obtained by suppressing reverberation from arbitrary artificial reverberant speech.

We address this problem by training speaker models using dereverberant speech obtained by suppressing early and late reverberation from arbitrary artificial reverberant speech.

Given Λ c and L, estimate the eigenphone matrices V(s) for each training speaker s using the corresponding speaker-dependent training data.

Then, the speaker-adapted model Λ s for each training speaker s is obtained using conventional speaker adaptation methods such as MLLR + MAP.

Since the majority of the adaptation parameters are the transformations, we estimate them on the training speakers and adapt to a new speaker by estimating the transform weights only.

The SSM which characterizes the a priori knowledge of the training speakers is effectively described in terms of the latent variable models such as the factor analysis or probabilistic principal component analysis.

Speaker recognition performance is evaluated on a subset of ten randomly selected speakers involving a total of 30 sentences per speaker (20 sentences for training speaker-dependent GMM and 10 sentences for testing).

Standard regression class tree-based MLLR was used to obtain the 100 training speakers' SA models.

Consequently, CNNs should have a smaller performance drop for test speakers whose voice is different from that of the training speakers.