Some attributes of the corpus are presented in Table 7. None of the speakers in the evaluation speech corpus is included in the acoustic training corpus described in Section 3.2.
Then the application of TM to the corpus is presented in three steps: pre-processing, model fitting, and post-processing.
The frequency distribution of the phonemes in the phonemic language corpus, is presented in Fig. 3. Fig. 3 Frequency distribution of the phoneme occurrence in Polish language.
In their article, a new named-entity-annotated tweet corpus was presented and the various tweet-specific linguistic phenomena were analyzed.
Following this, the analysis of the corpus is presented showing how, despite how relatively new the word is, it has established itself in certain patterns of use.
The frequency distribution of words in the orthographic language corpus, is presented in Fig. 2. Fig. 2 Frequency distribution of the word occurrence in Polish language.
Average mixed input SIRs of two channels on all simulated random matrices and for all test frames of each corpus is presented in the corresponding columns of Table 2. Like before, de-mixing matrices are estimated by running the proposed and baseline source separation methods.
First, corpus data are presented to show that complex noun phrase formation is a ubiquitous feature of engineering text, and that these phrases (or collocations) are highly discipline-specific in a way that individual words are not.
Moreover, the emotional categories present in both corpora are different, with the LDC corpus emphasising high-intensity 'prototypical' emotions and the Aibo corpus focussing on low-intensity 'non-prototypical' emotions.
The evaluation results on the HPO gold standard corpus (HPO GS) are presented in Table 2, while a more detailed overview according to the top-level HPO categories is depicted in Figure 3.
