Sentence examples for modeling of speech from inspiring English sources

There are typically several challenges in the studying and modeling of speech signals in the multivariate distribution case, e.g., the non-linear or linear inter-dimensional dependency, and the sparsity and complexity of the multidimensional space.

Recently, a number of works related to AM-FM modeling of speech as well as its applications to speech analysis and recognition recently have been reported [5 13].

Based on major advances in statistical modeling of speech in the 1980s, automatic speech recognition systems today find widespread application in tasks that require a human-machine interface.

Second, as the IFM method used for parameter estimation of copula-based distribution ends up in a sub-optimal estimate, developing an optimal parameter estimation method for large vector dimensions is needed to have a fair evaluation of the copula-based distribution power in the statistical modeling of speech signals, e.g., compared to the optimal parameter estimation method used for MLD and MGD.

As only one of parameter estimation methods of copula-based distribution, IFM method, was taken into account in the experimental evaluation, and the IFM method ends up a sub-optimal solution for parameter estimation, it is difficult to have a generic conclusion on copula-based distribution's benefit in statistical modeling of speech frame.

Our experiments show that, with a similar number of bases, source/filter modeling of speech in conjunction with the proposed noise constraints produces better separation results than sparse training of speech bases, even though the system is only designed for voiced speech and the results may still not be practical for many applications.

Figure 2 LP model of speech production.

A plethora of approaches utilize statistical models of speech signals.

Implications of the results are discussed with reference to different models of speech production.

The basis of LP analysis is the source-filter production model of speech.

This method has shown capability to construct accurate models of speech, even for short segments.