Sentence examples for training speakers from inspiring English sources

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

About 80% of the 578 training speakers (304 males, 274 females) were between 20 and 29 years old; less than 10% were over 40.

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

The set of linear basis vectors are obtained by a Kullback-Leibler divergence minimization algorithm for a closed set of training speakers.

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.

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

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

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

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

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