Sentence examples for distribution of speech from inspiring English sources

The distribution of speech errors was comparable across all experiments and exhibited syllable-position effects, suggesting an important role for production processes.

In this paper, we investigate multivariate distribution of speech signal in the time and transformed domains.

However, there are many statistical-based algorithms that take advantage of multivariate distribution of speech signals, and therefore, studying the multivariate distribution of speech to exploit a more proper pdf is a key issue for those speech processing algorithms too.

One of future work perspective might therefore be to study the power of statistical modeling of copula-based distribution of speech frame using optimal parameter estimation methods.

The evaluation results demonstrate that the multivariate distribution of speech signals in different domains is mostly super-Gaussian, except for Mel-frequency cepstral coefficient.

To study the multivariate distribution of speech features, two classes of pdfs are considered as the candidates in this paper: (1) copula-based distributions and (2) conventional distributions.

The utterances of TIMIT corpus are short (about 3.5 s), and around 90% of which are speech; this may introduce a bias when comparing the distributions of speech and non-speech.

A large research effort has focused on the investigation of a univariate case of speech signal distribution; however, in this paper, we study the multivariate distributions of speech signal and its representations using the conventional distribution functions, e.g., multivariate Gaussian and multivariate Laplace, and the copula-based multivariate distributions as candidates.

In Fig. 2, it is not difficult to find out that the distributions of speech and non-speech are more easily separated along with the increasing window length M. In corroboration, the speech classification error is reduced when increasing the order of the long-term window, as shown in Fig. 4.

These issues may have caused to mostly focus on the investigation of univariate distribution during the last two decades, and a small progress has been made in the multivariate distribution study of speech signal.

In the following, we develop a method to map the distribution of reverberant speech observations to the clean speech prior.