Sentence examples for simulation of speech from inspiring English sources

The intraoperative direct cortical simulation of speech eloquent brain tissue represents the standard of care in surgery of eloquent lesions [2, 13, 14].

These results demonstrate that distal learning with an articulatory synthesizer that incorporates knowledge of speech production mechanisms is an effective strategy for the simulation of speech production acquisition.

This strategy, when implemented with an articulatory synthesizer with sufficient capacity to generate acoustic data from model parameters, may have the potential to achieve the closest simulation of speech learning behavior.

Multi-2 consistently outperformed Multi-1, indicating that the close-talking speech from real users were more effective than the high-quality clean speech in the simulation of reverberant speech.

Another interesting observation is that the close-talking speech data collected from real users can be used for both the simulation of reverberant speech and dereverberation as the learning targets in joint training framework, instead of the high-quality clean speech data.

It consists of a detailed 3D model of the vocal tract that can be configured to fit the anatomy of any specific speaker, an advanced self-oscillating model of the vocal folds, and an efficient method for aeroacoustic simulation of the speech signal.

The effectiveness of the proposed scheme is verified by simulations for speech signals in different reverberant environments.

In the context of the current cognitive and neurobiological models, these processes can be described in terms of embodied mental simulation (as in Barsalou 2010) of speech acts subserved by action perception circuits including mirror neurons with specific neuropragmatic function (Pulvermüller and Fadiga 2010; Egorova et al. 2013).

For the convenience of computer simulation, speech files belonging to the same dialect region were concatenated to form a longer file.

To evaluate the performance of the JPBPF, a series of simulations are presented including nonlinear system identification, predicting of speech signals and nonlinear channel equalization.

After that, we chose another pair of speech signals to repeat the above simulations.