Sentence examples for modeling audio from inspiring English sources

Two levels of modeling, audio event and semantic context modeling, are devised to bridge the gap between physical audio features and semantic concepts.

While linear prediction (LP) has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals.

The chip does all the fancy audio engineering -- real-time acoustic modeling, audio beam forming and echo cancellation.

Mangu et al. [101] employed CNNs, DNNs, and GMMs as acoustic modeling, audio segmentation based on GMMs and DNNs, word lattices as ASR output, phone-WFST for keyword search, and system combination.

As for the shadowing model audio speed, gradual speed progression method was employed.

In Section 5, the approaches devoted to model audio background are investigated.

We propose a hierarchical approach that models audio events over a time series in order to accomplish semantic context detection.

Student's t distribution includes the Cauchy and Gaussian distributions as special cases and has been used to model audio sources [36, 37].

This is a convincing result, because the weekly speed progression of the model audio inevitably increases the difficulty level, especially for those with less proficient participants.

As an introduction of shadowing for first timers, in order to develop a solid foundation of the shadowing technique, monologic script in a steadily paced model audio is considered ideal to meet the accuracy-based objective.

In doing so, instead of using commercial audio materials (e.g. textbook CDs), the teaching instructor, who is a native Japanese speaker, recorded the model audio himself, in order to flexibly adjust the speech speed to suit best this study design.