Sentence examples for training audio from inspiring English sources

A training audio dataset of about 136 000 LSF vectors (approximately 3 minutes of audio) was used to estimate the parameters of a 16-class GMM.

In particular, we fine-tune a deep architecture originally trained for the Imagenet classification task, using a relatively small amount of data (almost 80 mins of training audio samples) along with data augmentation.

The feature extraction strategy is schematically depicted in Figure 2. As already mentioned, no segmentation/classification of the training audio-visual data is required in the proposed approach.

Gutierrez et al. [5, 17] used a training audio-visual database to generate a lookup table of audio-visual dynamic pairs.

Regardless of the method employed to model the audio-visual data and to compute facial movements from speech, the different approaches usually divide the training audio-visual data into classes.

The visual parameters of the training audio-visual data are classified into several visual clusters by k-means method, and for each cluster two individual HMMs are trained independently using the associated audio and visual parameters, and also a Fused HMM is trained.

The trained audio-visual model associated with the selected class is then used to estimate the corresponding visual parameters.

For the particular case of speech-driven facial animation, the animation is controlled by a set of visual features estimated from speech [5], by means of a trained audio-visual model [6].

The cinema was followed by a school for training in audio, visual and digital skills.

Miller et al. (2004) assessed MI skills after training with audio taped samples that lack non-verbal information [ 35].

To further increase the stability of a specific breathing phase, patient training using audio or visual feedback [ 18, 19] could be one aspect to improve multiple breath holds in several fractions.