Sentence examples for model compensation techniques from inspiring English sources

Fig. 2 Bayesian network representation of different model compensation techniques.

Various model compensation techniques exhibit two (more or less) distinct steps: First, the compensation parameters need to be estimated and, second, the actual compensation rule is applied to the acoustic model.

The figure is based on [5] Fig. 4 Bayesian network representations a and b of the decoding rule of [24], where the dashed links are disregarded in the different steps of the derivation (Subsection 4.6) Fig. 5 Bayesian network representation of a different model compensation techniques, b CMLLR (Subsection 6.3) and MLLR (Subsection 6.4), and c MAP adaptation (Subsection 6.5).

Although the presented work was motivated by the mentioned measurement application, the presented modelling, identification and compensation techniques are not limited to this application and can be transferred to many mechanical systems with friction.

Through delay time compensation techniques, the multi model multi-parametric Model Predictive Control (mp-MPC) is designed for pure dynamics of each linear region.

Such models can be used for software error compensation techniques in order to improve the machine's positioning behaviour as well as for diagnostic purposes.

Traditional servo design depends heavily on a precise system model so that frequency domain or time domain compensation techniques can be applied, and many synthesis tools such as LQG, H∞, or LMI, … are available based on this concept.

Although HEQ-MA has much larger computational complexity than feature compensation techniques, it can be still regarded as an efficient model adaptation technique compared to other more complex model adaptation techniques such as MLLR due to its predominantly simple algorithmic complexity.

This type of instability is impossible to compensate for with any of the standard compensation techniques.

Methods that modify the acoustic model are in general termed (acoustic) model-based or model compensation approaches and comprise inter alia the following sub-categories: so-called model adaptation techniques mostly update the parameters of the acoustic model, i.e., of the hidden Markov models (HMMs), prior to the decoding of a set of observed feature vectors.

SS techniques usually improve the quality and intelligibility of speech signal while speech recognition systems need compensation techniques to reduce mismatch between noisy speech features and clean trained acoustic model.